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Robotics
A Begginers Guide
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Contents
Articles
Robotics
1
Actuator
17
Electric motor
19
Tactile sensor
41
Computer vision
43
Mobile manipulator
51
Robot locomotion
53
Mobile robot
56
Robotic mapping
60
Human–robot interaction
61
Artificial intelligence
67
Outline of robotics
85
References
Article Sources and Contributors
101
Image Sources, Licenses and Contributors
104
Article Licenses
License
106
Robotics
Robotics
Robotics is the branch of technology that deals with the design,
construction, operation, and application of robots, as well as computer
systems for their control, sensory feedback, and information
processing. The design of a given robotic system will often incorporate
principles of mechanical engineering, electronic engineering, and
computer science (particularly artificial intelligence). The study of
biological systems often plays a key role in the systems engineering of
a project and also forms the field of bionics. The mathematical
expression of a biological system may give rise to control algorithms
for example, or by observing how a process is handled by nature, for
example the bifocal vision system, an analogous system may be
formed using electronics.
The concept of creating machines that can operate autonomously dates
back to classical times, but research into the functionality and potential
uses of robots did not grow substantially until the 20th century.
Throughout history, robotics has been often seen to mimic human
behavior, and often manage tasks in a similar fashion. Today, robotics
The Shadow robot hand system
is a rapidly growing field, as technological advances continue,
research, design, and building new robots serve various practical purposes, whether domestically, commercially, or
militarily. Many robots do jobs that are hazardous to people such as defusing bombs, mines and exploring
shipwrecks.
Etymology
The word robotics was derived from the word robot, which was introduced to the public by Czech writer Karel
Čapek in his play R.U.R. (Rossum's Universal Robots), which was published in 1920. The word robot comes from
the Slavic word robota, which means labour. The play begins in a factory that makes artificial people called robots,
creatures who can be mistaken for humans – similar to the modern ideas of androids. Karel Čapek himself did not
coin the word. He wrote a short letter in reference to an etymology in the Oxford English Dictionary in which he
named his brother Josef Čapek as its actual originator.
According to the Oxford English Dictionary, the word robotics was first used in print by Isaac Asimov, in his
science fiction short story "Liar!", published in May 1941 in Astounding Science Fiction. Asimov was unaware that
he was coining the term; since the science and technology of electrical devices is electronics, he assumed robotics
already referred to the science and technology of robots. In some of Asimov's other works, he states that the first use
of the word robotics was in his short story Runaround (Astounding Science Fiction, March 1942). However, the
original publication of "Liar!" predates that of "Runaround" by five months, so the former is generally cited as the
word's origin.
1
Robotics
2
History of robotics
In 1927 the Maschinenmensch ("machine-human") gynoid humanoid robot (also called "Parody", "Futura",
"Robotrix", or the "Maria impersonator") was the first depiction of a robot ever to appear on film was played by
German actress Brigitte Helm in Fritz Lang's film Metropolis.
In 1942 the science fiction writer Isaac Asimov formulated his Three Laws of Robotics.
In 1948 Norbert Wiener formulated the principles of cybernetics, the basis of practical robotics.
Fully autonomous robots only appeared in the second half of the 20th century. The first digitally operated and
programmable robot, the Unimate, was installed in 1961 to lift hot pieces of metal from a die casting machine and
stack them. Commercial and industrial robots are widespread today and used to perform jobs more cheaply, or more
accurately and reliably, than humans. They are also employed in jobs which are too dirty, dangerous, or dull to be
suitable for humans. Robots are widely used in manufacturing, assembly, packing and packaging, transport, earth
and space exploration, surgery, weaponry, laboratory research, safety, and the mass production of consumer and
industrial goods.
Date
Significance
Robot Name
Inventor
Third
One of the earliest descriptions of automata appears in the Lie Zi text, on a
century BC much earlier encounter between King Mu of Zhou (1023–957 BC) and a
and earlier mechanical engineer known as Yan Shi, an 'artificer'. The latter allegedly
presented the king with a life-size, human-shaped figure of his mechanical
handiwork.
Yan Shi
First
Descriptions of more than 100 machines and automata, including a fire engine,
century AD a wind organ, a coin-operated machine, and a steam-powered engine, in
and earlier Pneumatica and Automata by Heron of Alexandria
Ctesibius, Philo of
Byzantium, Heron of
Alexandria, and others
c. 420 BC
A wooden, steam propelled bird, which was able to fly
Archytas of Tarentum
1206
Created early humanoid automata, programmable automaton band
Robot band,
hand-washing automaton,
automated moving
[1]
peacocks
Al-Jazari
1495
Designs for a humanoid robot
Mechanical knight
Leonardo da Vinci
1738
Mechanical duck that was able to eat, flap its wings, and excrete
Digesting Duck
Jacques de Vaucanson
1898
Nikola Tesla demonstrates first radio-controlled vessel.
Teleautomaton
Nikola Tesla
1921
First fictional automatons called "robots" appear in the play R.U.R.
Rossum's Universal
Robots
Karel Čapek
1930s
Humanoid robot exhibited at the 1939 and 1940 World's Fairs
Elektro
Westinghouse Electric
Corporation
1948
Simple robots exhibiting biological behaviors
Elsie and Elmer
William Grey Walter
1956
First commercial robot, from the Unimation company founded by George
Devol and Joseph Engelberger, based on Devol's patents
Unimate
George Devol
1961
First installed industrial robot.
Unimate
George Devol
1973
First industrial robot with six electromechanically driven axes
Famulus
KUKA Robot Group
1974
The world’s first microcomputer controlled electric industrial robot, IRB 6 from IRB 6
ASEA, was delivered to a small mechanical engineering company in southern
Sweden. The design of this robot had been patented already 1972.
ABB Robot Group
1975
Programmable universal manipulation arm, a Unimation product
Victor Scheinman
[2]
PUMA
Robotics
3
Components
Power source
At present mostly (lead-acid) batteries are used as a power source. Many different types of batteries can be used as a
power source for robots. They range from lead acid batteries which are safe and have relatively long shelf lives but
are rather heavy to silver cadmium batteries that are much smaller in volume and are currently much more
expensive. Designing a battery powered robot needs to take into account factors such as safety, cycle lifetime and
weight. Generators, often some type of internal combustion engine, can also be used. However, such designs are
often mechanically complex and need fuel, require heat dissipation and are relatively heavy. A tether connecting the
robot to a power supply would remove the power supply from the robot entirely. This has the advantage of saving
weight and space by moving all power generation and storage components elsewhere. However, this design does
come with the drawback of constantly having a cable connected to the robot, which can be difficult to manage.
Potential power sources could be:
•
•
•
•
pneumatic (compressed gases)
hydraulics (liquids)
flywheel energy storage
organic garbage (through anaerobic digestion)
• faeces (human, animal); may be interesting in a military context as faeces of small combat groups may be reused
for the energy requirements of the robot assistant (see DEKA's project Slingshot Stirling engine on how the
system would operate)
Actuation
Actuators are like the "muscles" of a robot, the parts which convert
stored energy into movement. By far the most popular actuators are
electric motors that spin a wheel or gear, and linear actuators that
control industrial robots in factories. But there are some recent
advances in alternative types of actuators, powered by electricity,
chemicals, or compressed air.
Electric motors
The majority of robots use electric motors, often brushed and brushless
DC motors in portable robots, or AC motors in industrial robots and
CNC machines. These motors are often preferred in systems with
lighter loads, and where the predominant form of motion is rotational.
Linear actuators
Various types of linear actuators move in and out instead of rotating,
and often have quicker direction changes, particularly when very large
forces are needed such as with industrial robotics. They are typically
powered by compressed air (pneumatic actuator) or an oil (hydraulic
actuator).
Series elastic actuators
A robotic leg powered by air muscles
A spring can be designed as part of the motor actuator, to allow
improved force control. It has been used in various robots, particularly walking humanoid robots.
Robotics
Air muscles
Pneumatic artificial muscles, also known as air muscles, are special tubes that contract (typically up to 40%) when
air is forced inside them. They have been used for some robot applications.[3][4]
Muscle wire
Muscle wire, also known as Shape Memory Alloy, Nitinol or Flexinol Wire, is a material that contracts slightly
(typically under 5%) when electricity runs through it. They have been used for some small robot applications.
Electroactive polymers
EAPs or EPAMs are a new plastic material that can contract substantially (up to 380% activation strain) from
electricity, and have been used in facial muscles and arms of humanoid robots, and to allow new robots to float, fly,
swim or walk.
Piezo motors
Recent alternatives to DC motors are piezo motors or ultrasonic motors. These work on a fundamentally different
principle, whereby tiny piezoceramic elements, vibrating many thousands of times per second, cause linear or rotary
motion. There are different mechanisms of operation; one type uses the vibration of the piezo elements to walk the
motor in a circle or a straight line. Another type uses the piezo elements to cause a nut to vibrate and drive a screw.
The advantages of these motors are nanometer resolution, speed, and available force for their size. These motors are
already available commercially, and being used on some robots.
Elastic nanotubes
Elastic nanotubes are a promising artificial muscle technology in early-stage experimental development. The absence
of defects in carbon nanotubes enables these filaments to deform elastically by several percent, with energy storage
levels of perhaps 10 J/cm3 for metal nanotubes. Human biceps could be replaced with an 8 mm diameter wire of this
material. Such compact "muscle" might allow future robots to outrun and outjump humans.[5]
Sensing
Sensors allow robots to receive information about a certain measurement of the environment, or internal
components. This is essential for robots to perform their tasks, and act upon any changes in the environment to
calculate the appropriate response. They are used for various forms of measurements, to give the robots warnings
about safety or malfunctions, and to provide real time information of the task it is performing.
Touch
Current robotic and prosthetic hands receive far less tactile information than the human hand. Recent research has
developed a tactile sensor array that mimics the mechanical properties and touch receptors of human fingertips. The
sensor array is constructed as a rigid core surrounded by conductive fluid contained by an elastomeric skin.
Electrodes are mounted on the surface of the rigid core and are connected to an impedance-measuring device within
the core. When the artificial skin touches an object the fluid path around the electrodes is deformed, producing
impedance changes that map the forces received from the object. The researchers expect that an important function
of such artificial fingertips will be adjusting robotic grip on held objects.
Scientists from several European countries and Israel developed a prosthetic hand in 2009, called SmartHand, which
functions like a real one—allowing patients to write with it, type on a keyboard, play piano and perform other fine
movements. The prosthesis has sensors which enable the patient to sense real feeling in its fingertips.
4
Robotics
Vision
Computer vision is the science and technology of machines that see. As a scientific discipline, computer vision is
concerned with the theory behind artificial systems that extract information from images. The image data can take
many forms, such as video sequences and views from cameras.
In most practical computer vision applications, the computers are pre-programmed to solve a particular task, but
methods based on learning are now becoming increasingly common.
Computer vision systems rely on image sensors which detect electromagnetic radiation which is typically in the form
of either visible light or infra-red light. The sensors are designed using solid-state physics. The process by which
light propagates and reflects off surfaces is explained using optics. Sophisticated image sensors even require
quantum mechanics to provide a complete understanding of the image formation process. Robots can also be
equipped with multiple vision sensors to be better able to compute the sense of depth in the environment. Like
human eyes, robots' "eyes" must also be able to focus on a particular area of interest, and also adjust to variations in
light intensities.
There is a subfield within computer vision where artificial systems are designed to mimic the processing and
behavior of biological system, at different levels of complexity. Also, some of the learning-based methods developed
within computer vision have their background in biology.
Other
Other common forms of sensing in robotics use LIDAR, RADAR and SONAR.[citation needed]
Manipulation
Robots need to manipulate objects; pick up, modify,or otherwise have
an effect.Thus the "hands" of a robot are often referred to as end
effectors, while the "arm" is referred to as a manipulator. Most robot
arms have replaceable effectors, each allowing them to perform some
small range of tasks. Some have a fixed manipulator which cannot be
replaced, while a few have one very general purpose manipulator, for
example a humanoid hand.
For the definitive guide to all forms of robot end-effectors, their
design, and usage consult the book "Robot Grippers".[6]
Mechanical grippers
KUKA industrial robot operating in a foundry
One of the most common effectors is the gripper. In its simplest
manifestation it consists of just two fingers which can open and close
to pick up and let go of a range of small objects. Fingers can for example be made of a chain with a metal wire run
through it.[7] Hands that resemble and work more like a human hand include the Shadow Hand, the Robonaut
hand,[8] ... Hands that are of a mid-level complexity include the Delft hand.[9][10] An example of a simpler
mechanical gripper is Cornell's universal jamming gripper,[11] which does not use fingers but instead uses the
principle of granular jamming to switch the gripper from deformable to solid.
Mechanical grippers can come in various types, including friction and encompassing jaws. Friction jaws use all the
force of the gripper to hold the object in place using friction. Encompassing jaws cradle the object in place, using
less friction.
5
Robotics
6
Vacuum grippers
Vacuum grippers are very simple astrictive[12] devices, but can hold very large loads provided the prehension surface
is smooth enough to ensure suction.
Pick and place robots for electronic components and for large objects like car screens, often use very simple vacuum
grippers.
General purpose effectors
Some advanced robots are beginning to use fully humanoid hands, like the Shadow Hand, MANUS,[13] and the
Schunk hand. These are highly dexterous manipulators, with as many as 20 degrees of freedom and hundreds of
tactile sensors.[14]
Locomotion
Rolling robots
For simplicity most mobile robots have four wheels or a number of
continuous tracks. Some researchers have tried to create more complex
wheeled robots with only one or two wheels. These can have certain
advantages such as greater efficiency and reduced parts, as well as
allowing a robot to navigate in confined places that a four wheeled
robot would not be able to.
Two-wheeled balancing robots
Balancing robots generally use a gyroscope to detect how much a robot
is falling and then drive the wheels proportionally in the opposite
direction, to counterbalance the fall at hundreds of times per second,
based on the dynamics of an inverted pendulum. Many different
balancing robots have been designed. While the Segway is not
commonly thought of as a robot, it can be thought of as a component of
a robot, when used as such Segway refer to them as RMP (Robotic
Mobility Platform). An example of this use has been as NASA's
Robonaut that has been mounted on a Segway.
Segway in the Robot museum in Nagoya.
One-wheeled balancing robots
A one-wheeled balancing robot is an extension of a two-wheeled balancing robot so that it can move in any 2D
direction using a round ball as its only wheel. Several one-wheeled balancing robots have been designed recently,
such as Carnegie Mellon University's "Ballbot" that is the approximate height and width of a person, and Tohoku
Gakuin University's "BallIP". Because of the long, thin shape and ability to maneuver in tight spaces, they have the
potential to function better than other robots in environments with people.
Robotics
7
Spherical orb robots
Several attempts have been made in robots that are completely inside a spherical ball, either by spinning a weight
inside the ball, or by rotating the outer shells of the sphere. These have also been referred to as an orb bot or a ball
bot.
Six-wheeled robots
Using six wheels instead of four wheels can give better traction or grip in outdoor terrain such as on rocky dirt or
grass.
Tracked robots
Tank tracks provide even more traction than a six-wheeled robot.
Tracked wheels behave as if they were made of hundreds of wheels,
therefore are very common for outdoor and military robots, where the
robot must drive on very rough terrain. However, they are difficult to
use indoors such as on carpets and smooth floors. Examples include
NASA's Urban Robot "Urbie".[15]
Walking applied to robots
TALON military robots used by the United States
Army
Walking is a difficult and dynamic problem to solve. Several robots
have been made which can walk reliably on two legs, however none
have yet been made which are as robust as a human. There has been much study on human inspired walking, such as
AMBER lab which was established in 2008 by the Mechanical Engineering Department at Texas A&M
University.[16] Many other robots have been built that walk on more than two legs, due to these robots being
significantly easier to construct.[17][18] Walking robots can be used for uneven terrains, which would provide better
mobility and energy efficiency than other locomotion methods. Hybrids too have been proposed in movies such as I,
Robot, where they walk on 2 legs and switch to 4 (arms+legs) when going to a sprint. Typically, robots on 2 legs can
walk well on flat floors and can occasionally walk up stairs. None can walk over rocky, uneven terrain. Some of the
methods which have been tried are:
ZMP Technique
The Zero Moment Point (ZMP) is the algorithm used by robots such as Honda's ASIMO. The robot's onboard
computer tries to keep the total inertial forces (the combination of Earth's gravity and the acceleration and
deceleration of walking), exactly opposed by the floor reaction force (the force of the floor pushing back on the
robot's foot). In this way, the two forces cancel out, leaving no moment (force causing the robot to rotate and fall
over). However, this is not exactly how a human walks, and the difference is obvious to human observers, some of
whom have pointed out that ASIMO walks as if it needs the lavatory.[19] ASIMO's walking algorithm is not static,
and some dynamic balancing is used (see below). However, it still requires a smooth surface to walk on.
Hopping
Several robots, built in the 1980s by Marc Raibert at the MIT Leg Laboratory, successfully demonstrated very
dynamic walking. Initially, a robot with only one leg, and a very small foot, could stay upright simply by hopping.
The movement is the same as that of a person on a pogo stick. As the robot falls to one side, it would jump slightly in
that direction, in order to catch itself. Soon, the algorithm was generalised to two and four legs. A bipedal robot was
demonstrated running and even performing somersaults. A quadruped was also demonstrated which could trot, run,
pace, and bound. For a full list of these robots, see the MIT Leg Lab Robots [20] page.
Robotics
8
Dynamic balancing (controlled falling)
A more advanced way for a robot to walk is by using a dynamic balancing algorithm, which is potentially more
robust than the Zero Moment Point technique, as it constantly monitors the robot's motion, and places the feet in
order to maintain stability. This technique was recently demonstrated by Anybots' Dexter Robot, which is so stable,
it can even jump. Another example is the TU Delft Flame.
Passive dynamics
Perhaps the most promising approach utilizes passive dynamics where the momentum of swinging limbs is used for
greater efficiency. It has been shown that totally unpowered humanoid mechanisms can walk down a gentle slope,
using only gravity to propel themselves. Using this technique, a robot need only supply a small amount of motor
power to walk along a flat surface or a little more to walk up a hill. This technique promises to make walking robots
at least ten times more efficient than ZMP walkers, like ASIMO.
Other methods of locomotion
Flying
A modern passenger airliner is essentially a flying robot, with two
humans to manage it. The autopilot can control the plane for each stage
of the journey, including takeoff, normal flight, and even landing.
Other flying robots are uninhabited, and are known as unmanned aerial
vehicles (UAVs). They can be smaller and lighter without a human
pilot on board, and fly into dangerous territory for military surveillance
missions. Some can even fire on targets under command. UAVs are
also being developed which can fire on targets automatically, without
the need for a command from a human. Other flying robots include
cruise missiles, the Entomopter [21], and the Epson micro helicopter
robot [22]. Robots such as the Air Penguin, Air Ray, and Air Jelly have
lighter-than-air bodies, propelled by paddles, and guided by sonar.
Two robot snakes. Left one has 64 motors (with 2
degrees of freedom per segment), the right one
10.
Snaking
Several snake robots have been successfully developed. Mimicking the way real snakes move, these robots can
navigate very confined spaces, meaning they may one day be used to search for people trapped in collapsed
buildings. The Japanese ACM-R5 snake robot[23] can even navigate both on land and in water.[24]
Skating
A small number of skating robots have been developed, one of which is a multi-mode walking and skating device. It
has four legs, with unpowered wheels, which can either step or roll. Another robot, Plen, can use a miniature
skateboard or roller-skates, and skate across a desktop.
Robotics
9
Climbing
Several different approaches have been used to develop robots that
have the ability to climb vertical surfaces. One approach mimics the
movements of a human climber on a wall with protrusions; adjusting
the center of mass and moving each limb in turn to gain leverage. An
example of this is Capuchin,[25] built by Dr. Ruixiang Zhang [26] at
Stanford University, California. Another approach uses the specialized
toe pad method of wall-climbing geckoes, which can run on smooth
surfaces such as vertical glass. Examples of this approach include
Wallbot[27] and Stickybot.[28] China's "Technology Daily" November
15, 2008 reported New Concept Aircraft (ZHUHAI) Co., Ltd. Dr. Li
Hiu Yeung and his research group have recently successfully
developed the bionic gecko robot "Speedy Freelander". According to
Dr. Li introduction, this gecko robot can rapidly climbing up and down
Capuchin Climbing Robot
in a variety of building walls, ground and vertical wall fissure or
walking upside down on the ceiling, it is able to adapt on smooth glass, rough or sticky dust walls as well as the
various surface of metallic materials and also can automatically identify obstacles, circumvent the bypass and
flexible and realistic movements. Its flexibility and speed are comparable to the natural gecko. A third approach is to
mimic the motion of a snake climbing a pole[citation needed].
Swimming (Piscine)
It is calculated that when swimming some fish can achieve a propulsive efficiency greater than 90%. Furthermore,
they can accelerate and maneuver far better than any man-made boat or submarine, and produce less noise and water
disturbance. Therefore, many researchers studying underwater robots would like to copy this type of locomotion.
Notable examples are the Essex University Computer Science Robotic Fish, and the Robot Tuna built by the Institute
of Field Robotics [29], to analyze and mathematically model thunniform motion. The Aqua Penguin [30], designed
and built by Festo of Germany, copies the streamlined shape and propulsion by front "flippers" of penguins. Festo
have also built the Aqua Ray and Aqua Jelly, which emulate the locomotion of manta ray, and jellyfish, respectively.
Sailing
Sailboat robots have also been developed in order to make
measurements at the surface of the ocean. A typical sailboat robot is
Vaimos built by IFREMER and ENSTA-Bretagne. Since the
propulsion of sailboat robots uses the wind, the energy of the batteries
is only used for the computer, for the communication and for the
actuators (to tune the rudder and the sail). If the robot is equipped with
solar panel, the robot could theoretically navigate forever. The two
main competitions of sailboat robots are WRSC which takes place
every year in Europe and Sailbot [31].
The autonomous sailboat robot Vaimos
Robotics
10
Environmental interaction and navigation
Though a significant percentage of robots in commission today are
either human controlled, or operate in a static environment, there
is an increasing interest in robots that can operate autonomously in
a dynamic environment. These robots require some combination of
navigation hardware and software in order to traverse their
environment. In particular unforeseen events (e.g. people and other
obstacles that are not stationary) can cause problems or collisions.
Some highly advanced robots such as ASIMO, and Meinü robot
have particularly good robot navigation hardware and software.
Also, self-controlled cars, Ernst Dickmanns' driverless car, and the
entries in the DARPA Grand Challenge, are capable of sensing the
environment well and subsequently making navigational decisions
based on this information. Most of these robots employ a GPS
navigation device with waypoints, along with radar, sometimes
combined with other sensory data such as LIDAR, video cameras,
and inertial guidance systems for better navigation between waypoints.
RADAR, GPS, LIDAR, ... are all combined to provide
proper navigation and obstacle avoidance (vehicle
developed for 2007 DARPA Urban Challenge)
Human-robot interaction
If robots are to work effectively in homes and other non-industrial
environments, the way they are instructed to perform their jobs, and especially
how they will be told to stop will be of critical importance. The people who
interact with them may have little or no training in robotics, and so any interface
will need to be extremely intuitive. Science fiction authors also typically assume
that robots will eventually be capable of communicating with humans through
speech, gestures, and facial expressions, rather than a command-line interface.
Although speech would be the most natural way for the human to communicate,
it is unnatural for the robot. It will probably be a long time before robots interact
as naturally as the fictional C-3PO.
Speech recognition
Kismet can produce a range of facial
Interpreting the continuous flow of sounds coming from a human, in real time,
expressions.
is a difficult task for a computer, mostly because of the great variability of
speech.[32] The same word, spoken by the same person may sound different depending on local acoustics, volume,
the previous word, whether or not the speaker has a cold, etc.. It becomes even harder when the speaker has a
different accent.[33] Nevertheless, great strides have been made in the field since Davis, Biddulph, and Balashek
designed the first "voice input system" which recognized "ten digits spoken by a single user with 100% accuracy" in
1952.[34] Currently, the best systems can recognize continuous, natural speech, up to 160 words per minute, with an
accuracy of 95%.
Robotics
Robotic voice
Other hurdles exist when allowing the robot to use voice for interacting with humans. For social reasons, synthetic
voice proves suboptimal as a communication medium,[35] making it necessary to develop the emotional component
of robotic voice through various techniques.[36][37]
Gestures
One can imagine, in the future, explaining to a robot chef how to make a pastry, or asking directions from a robot
police officer. In both of these cases, making hand gestures would aid the verbal descriptions. In the first case, the
robot would be recognizing gestures made by the human, and perhaps repeating them for confirmation. In the second
case, the robot police officer would gesture to indicate "down the road, then turn right". It is likely that gestures will
make up a part of the interaction between humans and robots.
Facial expression
Facial expressions can provide rapid feedback on the progress of a dialog between two humans, and soon may be
able to do the same for humans and robots. Robotic faces have been constructed by Hanson Robotics using their
elastic polymer called Frubber, allowing a large number of facial expressions due to the elasticity of the rubber facial
coating and embedded subsurface motors (servos).[38] The coating and servos are built on a metal skull. A robot
should know how to approach a human, judging by their facial expression and body language. Whether the person is
happy, frightened, or crazy-looking affects the type of interaction expected of the robot. Likewise, robots like Kismet
and the more recent addition, Nexi[39] can produce a range of facial expressions, allowing it to have meaningful
social exchanges with humans.
Artificial emotions
Artificial emotions can also be generated, composed of a sequence of facial expressions and/or gestures. As can be
seen from the movie Final Fantasy: The Spirits Within, the programming of these artificial emotions is complex and
requires a large amount of human observation. To simplify this programming in the movie, presets were created
together with a special software program. This decreased the amount of time needed to make the film. These presets
could possibly be transferred for use in real-life robots.
Personality
Many of the robots of science fiction have a personality, something which may or may not be desirable in the
commercial robots of the future.[40] Nevertheless, researchers are trying to create robots which appear to have a
personality:[41][42] i.e. they use sounds, facial expressions, and body language to try to convey an internal state,
which may be joy, sadness, or fear. One commercial example is Pleo, a toy robot dinosaur, which can exhibit several
apparent emotions.[43]
11
Robotics
12
Control
The mechanical structure of a robot must be controlled to perform
tasks. The control of a robot involves three distinct phases –
perception, processing, and action (robotic paradigms). Sensors give
information about the environment or the robot itself (e.g. the position
of its joints or its end effector). This information is then processed to
calculate the appropriate signals to the actuators (motors) which move
the mechanical.
The processing phase can range in complexity. At a reactive level, it
may translate raw sensor information directly into actuator commands.
Sensor fusion may first be used to estimate parameters of interest (e.g.
the position of the robot's gripper) from noisy sensor data. An
immediate task (such as moving the gripper in a certain direction) is
inferred from these estimates. Techniques from control theory convert
the task into commands that drive the actuators.
At longer time scales or with more sophisticated tasks, the robot may
Puppet Magnus, a robot-manipulated marionette
with complex control systems
need to build and reason with a "cognitive" model. Cognitive models
try to represent the robot, the world, and how they interact. Pattern
recognition and computer vision can be used to track objects. Mapping techniques can be used to build maps of the
world. Finally, motion planning and other artificial intelligence techniques may be used to figure out how to act. For
example, a planner may figure out how to achieve a task without hitting obstacles, falling over, etc.
Autonomy levels
Control systems may also have varying levels of autonomy.
1. Direct interaction is used for haptic or tele-operated devices, and
the human has nearly complete control over the robot's motion.
2. Operator-assist modes have the operator commanding
medium-to-high-level tasks, with the robot automatically figuring
out how to achieve them.
3. An autonomous robot may go for extended periods of time without
human interaction. Higher levels of autonomy do not necessarily
require more complex cognitive capabilities. For example, robots in
assembly plants are completely autonomous, but operate in a fixed
pattern.
TOPIO, a humanoid robot, played ping pong at
Tokyo IREX 2009.
Another classification takes into account the interaction between human control and the machine motions.
1. Teleoperation. A human controls each movement, each machine actuator change is specified by the operator.
2. Supervisory. A human specifies general moves or position changes and the machine decides specific movements
of its actuators.
3. Task-level autonomy. The operator specifies only the task and the robot manages itself to complete it.
4. Full autonomy. The machine will create and complete all its tasks without human interaction.
Robotics
Robotics research
Much of the research in robotics focuses not on specific industrial tasks, but on investigations into new types of
robots, alternative ways to think about or design robots, and new ways to manufacture them but other investigations,
such as MIT's cyberflora project, are almost wholly academic.
A first particular new innovation in robot design is the opensourcing of robot-projects. To describe the level of
advancement of a robot, the term "Generation Robots" can be used. This term is coined by Professor Hans Moravec,
Principal Research Scientist at the Carnegie Mellon University Robotics Institute in describing the near future
evolution of robot technology. First generation robots, Moravec predicted in 1997, should have an intellectual
capacity comparable to perhaps a lizard and should become available by 2010. Because the first generation robot
would be incapable of learning, however, Moravec predicts that the second generation robot would be an
improvement over the first and become available by 2020, with the intelligence maybe comparable to that of a
mouse. The third generation robot should have the intelligence comparable to that of a monkey. Though fourth
generation robots, robots with human intelligence, professor Moravec predicts, would become possible, he does not
predict this happening before around 2040 or 2050.[44]
The second is Evolutionary Robots. This is a methodology that uses evolutionary computation to help design robots,
especially the body form, or motion and behavior controllers. In a similar way to natural evolution, a large
population of robots is allowed to compete in some way, or their ability to perform a task is measured using a fitness
function. Those that perform worst are removed from the population, and replaced by a new set, which have new
behaviors based on those of the winners. Over time the population improves, and eventually a satisfactory robot may
appear. This happens without any direct programming of the robots by the researchers. Researchers use this method
both to create better robots, and to explore the nature of evolution. Because the process often requires many
generations of robots to be simulated, this technique may be run entirely or mostly in simulation, then tested on real
robots once the evolved algorithms are good enough.[45] Currently, there are about 1 million industrial robots toiling
around the world, and Japan is the top country having high density of utilizing robots in its manufacturing
industry.[citation needed]
Dynamics and kinematics
The study of motion can be divided into kinematics and dynamics. Direct kinematics refers to the calculation of end
effector position, orientation, velocity, and acceleration when the corresponding joint values are known. Inverse
kinematics refers to the opposite case in which required joint values are calculated for given end effector values, as
done in path planning. Some special aspects of kinematics include handling of redundancy (different possibilities of
performing the same movement), collision avoidance, and singularity avoidance. Once all relevant positions,
velocities, and accelerations have been calculated using kinematics, methods from the field of dynamics are used to
study the effect of forces upon these movements. Direct dynamics refers to the calculation of accelerations in the
robot once the applied forces are known. Direct dynamics is used in computer simulations of the robot. Inverse
dynamics refers to the calculation of the actuator forces necessary to create a prescribed end effector acceleration.
This information can be used to improve the control algorithms of a robot.
In each area mentioned above, researchers strive to develop new concepts and strategies, improve existing ones, and
improve the interaction between these areas. To do this, criteria for "optimal" performance and ways to optimize
design, structure, and control of robots must be developed and implemented.
13
Robotics
14
Education and training
Robotics engineers design robots, maintain them, develop new
applications for them, and conduct research to expand the potential of
robotics. Robots have become a popular educational tool in some
middle and high schools, as well as in numerous youth summer camps,
raising interest in programming, artificial intelligence and robotics
among students. First-year computer science courses at several
universities now include programming of a robot in addition to
traditional software engineering-based coursework. On the Technion
I&M faculty an educational laboratory was established in 1994 by Dr.
Jacob Rubinovitz.
Career training
Universities offer bachelors, masters, and doctoral degrees in the field
of robotics. Vocational schools offer robotics training aimed at careers
in robotics.
Certification
The Robotics Certification Standards Alliance (RCSA) is an
international robotics certification authority that confers various
industry- and educational-related robotics certifications.
Summer robotics camp
The SCORBOT-ER 4u – educational robot.
Several national summer camp programs include robotics as part of their core curriculum, including Digital Media
Academy, RoboTech, and Cybercamps. In addition, youth summer robotics programs are frequently offered by
celebrated museums such as the American Museum of Natural History[46] and The Tech Museum of Innovation in
Silicon Valley, CA, just to name a few. An educational robotics lab also exists at the IE & mgmnt Faculty of the
Technion. It was created by Dr. Jacob Rubinovitz.
Robotics
15
Robotics afterschool programs
Many schools across the country are beginning to add robotics programs to their after school curriculum. Three main
programs for afterschool robotics are Botball, FIRST Robotics Competition, and Vex Robotics Design System.
Employment
Robotics is an essential component in many modern manufacturing
environments. As factories increase their use of robots, the number of
robotics–related jobs grow and have been observed to be steadily
rising.
References
[1] al-Jazari (Islamic artist) (http:/ / www. britannica. com/ eb/ topic-301961/ al-Jazari),
Encyclopædia Britannica.
[2] Imitation of Life: A History of the First Robots (http:/ / www. cerebromente. org. br/
n09/ historia/ turtles_i. htm)
[3] Air Muscles from Image Company (http:/ / www. imagesco. com/ articles/
airmuscle/ AirMuscleDescription06. html)
[4] Air Muscles from Shadow Robot (http:/ / www. shadowrobot. com/ airmuscles/
overview. shtml)
[5] John D. Madden, 2007, /science.1146351
[6] G.J. Monkman, S. Hesse, R. Steinmann & H. Schunk – Robot Grippers – Wiley,
Berlin 2007
[7] Discovery Channel's Mythbusters making mechanical gripper from chain and metal
wire (http:/ / kwc. org/ mythbusters/ 2007/ 04/ episode_78_ninja_myths_walking.
html)
A robot technician builds small all-terrain robots.
(Courtesy: MobileRobots Inc)
[8] Robonaut hand (http:/ / er. jsc. nasa. gov/ seh/ Robotics/ index. html)
[9] Delft hand by TU Delft (http:/ / www. dbl. tudelft. nl/ over-de-faculteit/ afdelingen/ biomechanical-engineering/ onderzoek/
dbl-delft-biorobotics-lab/ delft-arm-and-hand/ )
[10] Delft hand by Gert Kragten (http:/ / tudelft. nl/ nl/ actueel/ laatste-nieuws/ artikel/ detail/
tu-delft-ontwikkelt-goedkope-voorzichtige-robothand/ )
[11] Universal jamming gripper (http:/ / creativemachines. cornell. edu/ jamming_gripper)
[12] Definition "astrictive" (to bind, confine, or constrict) in Collins English Dictionary & Thesaurus (http:/ / dictionary. reverso. net/
english-definitions/ astrictive)
[13] MANUS (http:/ / ieeexplore. ieee. org/ Xplore/ login. jsp?url=http:/ / ieeexplore. ieee. org/ iel5/ 10041/ 32216/ 01501097.
pdf?arnumber=1501097& authDecision=-203)
[14] Shadowrobot.com (http:/ / www. shadowrobot. com/ )
[15] JPL Robotics: System: Commercial Rovers (http:/ / www-robotics. jpl. nasa. gov/ systems/ system. cfm?System=4#urbie)
[16] AMBER lab (http:/ / www. bipedalrobotics. com)
[17] Multipod robots easy to construct (http:/ / www. hexapodrobot. com/ index. html)
[18] AMRU-5 hexapod robot (http:/ / mecatron. rma. ac. be/ pub/ 2005/ ISMCR05_verlinden. pdf)
[19] Vtec Forum: A drunk robot? thread (http:/ / motegi. vtec. net/ forums/ one-message?message_id=131434& news_item_id=129834)
[20] http:/ / www. ai. mit. edu/ projects/ leglab/ robots/ robots-main-bottom. html
[21] http:/ / www-robotics. usc. edu/ ~avatar/
[22] http:/ / www. epson. co. jp/ e/ newsroom/ news_2004_08_18. htm
[23] ACM-R5 (http:/ / www-robot. mes. titech. ac. jp/ robot/ snake/ acm-r5/ acm-r5_e. html)
[24] Swimming snake robot (commentary in Japanese) (http:/ / video. google. com/ videoplay?docid=139523333240485714)
[25] Capuchin (http:/ / www. youtube. com/ watch?v=JzHasc4Vhm8& feature=channel) at YouTube
[26] http:/ / ai. stanford. edu/ ~rxzhang/
[27] Wallbot (http:/ / www. youtube. com/ watch?v=Tq8Yw19bn7Q& feature=related) at YouTube
[28] Stanford University: Stickybot (http:/ / www. youtube. com/ watch?v=k2kZk6riGWU)
[29] http:/ / fibo. kmutt. ac. th/
[30] http:/ / www. youtube. com/ watch?v=E8B4_fGopzw& feature=related
[31] http:/ / www. sailbot. org/
Robotics
[32] J. Norberto Pires, (2005). "Robot-by-voice: experiments on commanding an industrial robot using the human voice", Industrial Robot: An
International Journal, Vol. 32, Issue 6, pp. 505–511, . Available: online (http:/ / www. emeraldinsight. com/ journals.
htm?articleid=1528883& show=abstract) and pdf (http:/ / www. smerobot. org/ 08_scientific_papers/ papers/ Pires_Ind-Robot-Journ_2005.
pdf)
[33] Survey of the State of the Art in Human Language Technology: 1.2: Speech Recognition (http:/ / cslu. cse. ogi. edu/ HLTsurvey/ ch1node4.
html)
[34] Fournier, Randolph Scott., and B. June. Schmidt. "Voice Input Technology: Learning Style and Attitude Toward Its Use." Delta Pi Epsilon
Journal 37 (1995): 1_12.
[35] M.L. Walters, D.S. Syrdal, K.L. Koay, K. Dautenhahn, R. te Boekhorst, (2008). Human approach distances to a mechanical-looking robot
with different robot voice styles. In: Proceedings of the 17th IEEE International Symposium on Robot and Human Interactive Communication,
2008. RO-MAN 2008, Munich, 1–3 Aug. 2008, pp. 707–712, . Available: online (http:/ / ieeexplore. ieee. org/ xpl/ freeabs_all.
jsp?arnumber=4600750) and pdf (https:/ / uhra. herts. ac. uk/ dspace/ bitstream/ 2299/ 2352/ 1/ 902503. pdf)
[36] Sandra Pauletto, Tristan Bowles, (2010). Designing the emotional content of a robotic speech signal. In: Proceedings of the 5th Audio
Mostly Conference: A Conference on Interaction with Sound, New York, ISBN 978-1-4503-0046-9, . Available: online (http:/ / portal. acm.
org/ citation. cfm?id=1859799. 1859804)
[37] Tristan Bowles, Sandra Pauletto, (2010). Emotions in the Voice: Humanising a Robotic Voice. In: Proceedings of the 7th Sound and Music
Computing Conference, Barcelona, Spain.
[38] Frubber facial expressions (http:/ / www. hansonrobotics. com/ innovations. html)
[39] Nexi facial expressions (http:/ / www. time. com/ time/ specials/ packages/ article/ 0,28804,1852747_1854195_1854135,00. html)
[40] (Park et al. 2005) Synthetic Personality in Robots and its Effect on Human-Robot Relationship (http:/ / www. cs. ubc. ca/ ~van/ GI2005/
Posters/ GI_abstract. pdf)
[41] National Public Radio: Robot Receptionist Dishes Directions and Attitude (http:/ / www. npr. org/ templates/ story/ story.
php?storyId=5067678)
[42] New Scientist: A good robot has personality but not looks (http:/ / viterbi. usc. edu/ tools/ download/ ?asset=/ assets/ 023/ 49186. pdf&
name=nsmaja. pdf)
[43] Ugobe: Introducing Pleo (http:/ / www. ugobe. com/ pleo/ index. html)
[44] NOVA conversation with Professor Moravec, October, 1997. NOVA Online (http:/ / www. pbs. org/ wgbh/ nova/ robots/ moravec. html)
[45] The Latest Technology Research News: Evolution trains robot teams (http:/ / www. trnmag. com/ Stories/ 2004/ 051904/
Evolution_trains_robot_teams_051904. html)
[46] Education at American Museum of Natural History (http:/ / www. amnh. org/ education/ students/ offering. php?id=534)
Bibliography
• K. S. Fu & R.C. Gonzalez & C.S.G. Lee, Robotics: Control, Sensing, Vision, and Intelligence (CAD/CAM,
robotics, and computer vision)
• C.S.G. Lee & R.C. Gonzalez & K.S. Fu, Tutorial on robotics
• "SP200 With Open Control Center. Robotic Prescription Dispensing System" (http://www.scriptpro.com/
products/sp-200/SP_200_OCC_Low_Res.pdf), accessed November 22, 2008.
• "McKesson Empowering HealthCare. Robot RX" (http://www.mckesson.com/en_us/McKesson.com/For+
Pharmacies/Inpatient/Pharmacy+Automation/ROBOT-Rx.html), accessed November 22, 2008.
• "Aethon. You Deliver the Care. TUG Delivers the Rest" (http://aethon.com/brochure.pdf), accessed November
22, 2008.Wikipedia:Link rot
• Waukee Robotics Club (http://www.waukeerobotics.com)
• Marco Ceccarelli, "Fundamentals of Mechanics of Robotic Manipulators"
16
Robotics
Further reading
• Journal of Field Robotics (http://www3.interscience.wiley.com/journal/117946193/grouphome/home.html)
• R. Andrew Russell (1990). Robot Tactile Sensing. New York: Prentice Hall. ISBN 0-13-781592-1
External links
•
•
•
•
Robotics (http://www.dmoz.org/Computers/Robotics/) on the Open Directory Project
Harvard Graduate School of Design, Design Robotics Group (http://research.gsd.harvard.edu/drg/)
The Robotics Institute at Carnegie Mellon University (http://www.ri.cmu.edu/)
Biologically Inspired Robotics Lab, Case Western Reserve University (http://biorobots.case.edu/)
Actuator
An actuator is a type of motor for moving or controlling a mechanism or system. It is operated by a source of
energy, typically electric current, hydraulic fluid pressure, or pneumatic pressure, and converts that energy into
motion. An actuator is the mechanism by which a control system acts upon an environment. The control system can
be simple (a fixed mechanical or electronic system), software-based (e.g. a printer driver, robot control system), or a
human or other agent.
History
Some of the earliest forms of actuation can be found as far back as Archimedes, who lived approximately between
the years 287 B.C., and 212 B.C. What became known as Archimedes' screw was one of the first linear actuators
used to haul water from boats.
Other early actuation methods included mechanisms with wooden screws designed to crush grapes into wine and
olives into oil.
Types
A hydraulic actuator consists of a cylinder or fluid motor that uses hydraulic power to facilitate mechanical
operation. The mechanical motion gives an output in terms of linear, rotary or oscillatory motion. Because liquid
cannot be compressed, a hydraulic actuator can exert considerable force, but is limited in acceleration and speed.
A pneumatic actuator converts energy formed by compressed air at high pressure into either linear or rotary
motion. Pneumatic energy is desirable for main engine controls because it can quickly respond in starting and
stopping as the power source does not need to be stored in reserve for operation.
An electric actuator is powered by motor that converts electrical energy to mechanical torque. The electrical energy
is used to actuate equipment such as multi-turn valves. It is one of the cleanest and most readily available forms of
actuator because it does not involve oil.
A mechanical actuator functions by converting rotary motion into linear motion to execute movement. It involves
gears, rails, pulleys, chains and other devices to operate.
17
Actuator
Examples and applications
In engineering, actuators are frequently used as mechanisms to introduce motion, or to clamp an object so as to
prevent motion. In electronic engineering, actuators are a subdivision of transducers. They are devices which
transform an input signal (mainly an electrical signal) into motion. Electrical motors, pneumatic actuators, hydraulic
pistons, relays, comb drives, piezoelectric actuators, thermal bimorphs, digital micromirror devices and electroactive
polymers are some examples of such actuators.
Motors are mostly used when circular motions are needed, but can also be used for linear applications by
transforming circular to linear motion with a lead screw or similar mechanism. On the other hand, some actuators are
intrinsically linear, such as piezoelectric actuators. Conversion between circular and linear motion is commonly
made via a few simple types of mechanism including:
• Screw: Screw jack, ball screw and roller screw actuators all operate on the principle of the simple machine known
as the screw. By rotating the actuator's nut, the screw shaft moves in a line. By moving the screw shaft, the nut
rotates.
• Wheel and axle: Hoist, winch, rack and pinion, chain drive, belt drive, rigid chain and rigid belt actuators operate
on the principle of the wheel and axle. By rotating a wheel/axle (e.g. drum, gear, pulley or shaft) a linear member
(e.g. cable, rack, chain or belt) moves. By moving the linear member, the wheel/axle rotates.[1]
In virtual instrumentation, actuators and sensors are the hardware complements of virtual instruments.
Performance Metrics
Performance metrics for actuators include speed, acceleration, and force (alternatively, angular speed, angular
acceleration, and torque), as well as energy efficiency and considerations such as mass, volume, operating
conditions, and durability, among others.
Force
When considering force in actuators for applications, two main metrics should be considered. These two are static
and dynamic loads. Static load is the force capability of the actuator while not in motion. Conversely, the dynamic
load of the actuator is the force capability while in motion. The two aspects are rarely have the weight capability and
must be considered separately.
Speed
Speed should be considered primarily at a no-load pace, since the speed will invariably decrease as the load amount
increases. The rate the speed will decrease will directly correlate with the amount of force and the initial speed.
Operating Conditions
Actuators are commonly rated using the standard IP rating system. Those that are rated for dangerous environments
will have a higher IP rating than those for personal or common industrial use.
Durability
This will be determined by each individual manufacturer, depending on usage and quality.
18
Actuator
19
References
[1] Sclater, N., Mechanisms and Mechanical Devices Sourcebook, 4th Edition (2007), 25, McGraw-Hill
External links
• Automotive Actuators (http://www.cvel.clemson.edu/auto/actuators/auto-actuators.html)
Electric motor
An electric motor is an electric machine that converts electrical energy
into mechanical energy.
In normal motoring mode, most electric motors operate through the
interaction between an electric motor's magnetic field and winding
currents to generate force within the motor. In certain applications,
such as in the transportation industry with traction motors, electric
motors can operate in both motoring and generating or braking modes
to also produce electrical energy from mechanical energy.
Found in applications as diverse as industrial fans, blowers and pumps,
machine tools, household appliances, power tools, and disk drives,
electric motors can be powered by direct current (DC) sources, such as
from batteries, motor vehicles or rectifiers, or by alternating current
Various electric motors, compared to 9 V battery.
(AC) sources, such as from the power grid, inverters or generators.
Small motors may be found in electric watches. General-purpose
motors with highly standardized dimensions and characteristics provide convenient mechanical power for industrial
use. The largest of electric motors are used for ship propulsion, pipeline compression and pumped-storage
applications with ratings reaching 100 megawatts. Electric motors may be classified by electric power source type,
internal construction, application, type of motion output, and so on.
Devices such as magnetic solenoids and loudspeakers that convert electricity into motion but do not generate usable
mechanical power are respectively referred to as actuators and transducers. Electric motors are used to produce linear
force or torque (rotary).
History
Cutaway view through stator of induction motor.
Electric motor
20
Early motors
Perhaps the first electric motors were simple electrostatic devices created by
the Scottish monk Andrew Gordon in the 1740s.[1] The theoretical principle
behind production of mechanical force by the interactions of an electric
current and a magnetic field, Ampère's force law, was discovered later by
André-Marie Ampère in 1820. The conversion of electrical energy into
mechanical energy by electromagnetic means was demonstrated by the
British scientist Michael Faraday in 1821. A free-hanging wire was dipped
into a pool of mercury, on which a permanent magnet (PM) was placed.
When a current was passed through the wire, the wire rotated around the
magnet, showing that the current gave rise to a close circular magnetic field
around the wire. This motor is often demonstrated in physics experiments,
brine substituting for toxic mercury. Though Barlow's wheel was an early
refinement to this Faraday demonstration, these and similar homopolar
motors were to remain unsuited to practical application until late in the century.
In 1827, Hungarian physicist Ányos Jedlik started experimenting
with electromagnetic coils. After Jedlik solved the technical
problems of the continuous rotation with the invention of
commutator, he called his early devices as "electromagnetic
self-rotors". Although they were used only for instructional
purposes, in 1828 Jedlik demonstrated the first device to contain
the three main components of practical DC motors: the stator,
rotor and commutator. The device employed no permanent
magnets, as the magnetic fields of both the stationary and
revolving components were produced solely by the currents
flowing through their windings.
Faraday's electromagnetic experiment,
1821
Jedlik's "electromagnetic self-rotor", 1827 (Museum of
Applied Arts, Budapest). The historic motor still works
perfectly today.
Success with DC motors
The first commutator DC electric motor capable of turning machinery was invented by the British scientist William
Sturgeon in 1832. Following Sturgeon's work, a commutator-type direct-current electric motor made with the
intention of commercial use was built by the American inventor Thomas Davenport, which he patented in 1837. The
motors ran at up to 600 revolutions per minute, and powered machine tools and a printing press. Due to the high cost
of primary battery power, the motors were commercially unsuccessful and Davenport went bankrupt. Several
inventors followed Sturgeon in the development of DC motors but all encountered the same battery power cost
issues. No electricity distribution had been developed at the time. Like Sturgeon's motor, there was no practical
commercial market for these motors.
In 1855, Jedlik built a device using similar principles to those used in his electromagnetic self-rotors that was
capable of useful work. He built a model electric vehicle that same year.
The first commercially successful DC motors followed the invention by Zénobe Gramme who had in 1871
developed the anchor ring dynamo which solved the double-T armature pulsating DC problem. In 1873, Gramme
found that this dynamo could be used as a motor, which he demonstrated to great effect at exhibitions in Vienna and
Philadelphia by connecting two such DC motors at a distance of up to 2 km away from each other, one as a
generator. (See also 1873 : l'expérience décisive [Decisive Workaround] .)
In 1886, Frank Julian Sprague invented the first practical DC motor, a non-sparking motor that maintained relatively
constant speed under variable loads. Other Sprague electric inventions about this time greatly improved grid electric
Electric motor
distribution (prior work done while employed by Thomas Edison), allowed power from electric motors to be
returned to the electric grid, provided for electric distribution to trolleys via overhead wires and the trolley pole, and
provided controls systems for electric operations. This allowed Sprague to use electric motors to invent the first
electric trolley system in 1887–88 in Richmond VA, the electric elevator and control system in 1892, and the electric
subway with independently powered centrally controlled cars, which were first installed in 1892 in Chicago by the
South Side Elevated Railway where it became popularly known as the "L". Sprague's motor and related inventions
led to an explosion of interest and use in electric motors for industry, while almost simultaneously another great
inventor was developing its primary competitor, which would become much more widespread. The development of
electric motors of acceptable efficiency was delayed for several decades by failure to recognize the extreme
importance of a relatively small air gap between rotor and stator. Efficient designs have a comparatively small air
gap. [2] The St. Louis motor, long used in classrooms to illustrate motor principles, is extremely inefficient for the
same reason, as well as appearing nothing like a modern motor.
Application of electric motors revolutionized industry. Industrial processes were no longer limited by power
transmission using line shafts, belts, compressed air or hydraulic pressure. Instead every machine could be equipped
with its own electric motor, providing easy control at the point of use, and improving power transmission efficiency.
Electric motors applied in agriculture eliminated human and animal muscle power from such tasks as handling grain
or pumping water. Household uses of electric motors reduced heavy labor in the home and made higher standards of
convenience, comfort and safety possible. Today, electric motors stand for more than half of the electric energy
consumption in the US.
Emergence of AC motors
In 1824, the French physicist François Arago formulated the existence of rotating magnetic fields, termed Arago's
rotations, which, by manually turning switches on and off, Walter Baily demonstrated in 1879 as in effect the first
primitive induction motor. In the 1880s, many inventors were trying to develop workable AC motors because AC's
advantages in long distance high voltage transmission were counterbalanced by the inability to operate motors on
AC. Practical rotating AC induction motors were independently invented by Galileo Ferraris and Nikola Tesla, a
working motor model having been demonstrated by the former in 1885 and by the latter in 1887. In 1888, the Royal
Academy of Science of Turin published Ferraris's research detailing the foundations of motor operation while
however concluding that "the apparatus based on that principle could not be of any commercial importance as
motor." In 1888, Tesla presented his paper A New System for Alternating Current Motors and Transformers to the
AIEE that described three patented two-phase four-stator-pole motor types: one with a four-pole rotor forming a
non-self-starting reluctance motor, another with a wound rotor forming a self-starting induction motor, and the third
a true synchronous motor with separately excited DC supply to rotor winding. One of the patents Tesla filed in 1887,
however, also described a shorted-winding-rotor induction motor. George Westinghouse promptly bought Tesla's
patents, employed Tesla to develop them, and assigned C. F. Scott to help Tesla, Tesla leaving for other pursuits in
1889. The constant speed AC induction motor was found not to be suitable for street cars but Westinghouse
engineers successfully adapted it to power a mining operation in Telluride, Colorado in 1891. Steadfast in his
promotion of three-phase development, Mikhail Dolivo-Dobrovolsky invented the three-phase cage-rotor induction
motor in 1889 and the three-limb transformer in 1890. This type of motor is now used for the vast majority of
commercial applications. However, he claimed that Tesla's motor was not practical because of two-phase pulsations,
which prompted him to persist in his three-phase work. Although Westinghouse achieved its first practical induction
motor in 1892 and developed a line of polyphase 60 hertz induction motors in 1893, these early Westinghouse
motors were two-phase motors with wound rotors until B. G. Lamme developed a rotating bar winding rotor. The
General Electric Company began developing three-phase induction motors in 1891. By 1896, General Electric and
Westinghouse signed a cross-licensing agreement for the bar-winding-rotor design, later called the squirrel-cage
rotor. Induction motor improvements flowing from these inventions and innovations were such that a 100
horsepower (HP) induction motor currently has the same mounting dimensions as a 7.5 HP motor in 1897.
21
Electric motor
22
Motor construction
Rotor
In an electric motor the moving part is the rotor which turns the shaft
to deliver the mechanical power. The rotor usually has conductors laid
into it which carry currents that interact with the magnetic field of the
stator to generate the forces that turn the shaft. However, some rotors
carry permanent magnets, and the stator holds the conductors.
Electric motor rotor (left) and stator (right)
Stator
The stationary part is the stator, usually has either windings or permanent magnets.
Air gap
In between the rotor and stator is the air gap. The air gap has important effects, and is generally as small as possible,
as a large gap has a strong negative effect on the performance of an electric motor.
Windings
Windings are wires that are laid in coils, usually wrapped around a laminated soft iron magnetic core so as to form
magnetic poles when energised with current.
Electric machines come in two basic magnet field pole configurations: salient-pole machine and nonsalient-pole
machine. In the salient-pole machine the pole's magnetic field is produced by a winding wound around the pole
below the pole face. In the nonsalient-pole, or distributed field, or round-rotor, machine, the winding is distributed in
pole face slots. A shaded-pole motor has a winding around part of the pole that delays the phase of the magnetic field
for that pole.
Some motors have conductors which consist of thicker metal, such as bars or sheets of metal, usually copper,
although sometimes aluminum is used. These are usually powered by electromagnetic induction.
Commutator
A commutator is a mechanism used to switch the input of certain AC
and DC machines consisting of slip ring segments insulated from each
other and from the electric motor's shaft. The motor's armature current
is supplied through the stationary brushes in contact with the revolving
commutator, which causes required current reversal and applies power
to the machine in an optimal manner as the rotor rotates from pole to
pole.[3][4] In absence of such current reversal, the motor would brake to
Toy's small DC motor with commutator.
a stop. In light of significant advances in the past few decades due to
improved technologies in electronic controller, sensorless control,
induction motor, and permanent magnet motor fields, electromechanically commutated motors are increasingly
being displaced by externally commutated induction and permanent magnet motors.
Electric motor
23
Motor supply and control
Motor supply
A DC motor is usually supplied through slip ring commutator as described above. AC motors' commutation can be
either slip ring commutator or externally commutated type, can be fixed-speed or variable-speed control type, and
can be synchronous or asynchronous type. Universal motors can run on either AC or DC.
Motor control
Fixed-speed controlled AC motors are provided with direct-on-line or soft-start starters.
Variable speed controlled AC motors are provided with a range of different power inverter, variable-frequency drive
or electronic commutator technologies.
The term electronic commutator is usually associated with self-commutated brushless DC motor and switched
reluctance motor applications.
Major categories
Electric motors operate on three different physical principles: magnetic, electrostatic and piezoelectric. By far the
most common is magnetic.
In magnetic motors, magnetic fields are formed in both the rotor and the stator. The product between these two fields
gives rise to a force, and thus a torque on the motor shaft. One, or both, of these fields must be made to change with
the rotation of the motor. This is done by switching the poles on and off at the right time, or varying the strength of
the pole.
The main types are DC motors and AC motors, the former increasingly being displaced by the latter.
AC electric motors are either asynchronous and synchronous.
Once started, a synchronous motor requires synchronism with the moving magnetic field's synchronous speed for all
normal torque conditions.
In synchronous machines, the magnetic field must be provided by means other than induction such as from
separately excited windings or permanent magnets.
It is usual to distinguish motors' rated output power about the unity horsepower threshold so that integral horsepower
refers to motor(s) equal to or above, and fractional horsepower (FHP) refers to motor(s) below, the threshold.
Major Categories
Self-Commutated
MechanicalCommutator Motors
[6]
AC
DC
Externally Commutated
ElectronicCommutator
(EC)
[5]
Motors
AC5, 6
Asynchronous
Machines
Synchronous
Machines2
AC6
Electric motor
24
* Universal motor
(AC commutator
series motor or
AC/DC motor)1
* Repulsion motor
Simple electronics
Electrically
excited DC
motor:
* Separately
excited
* Series
* Shunt
* Compound
PM DC motor
Rectifier,
linear
transistor(s)
or DC chopper
With PM rotor:
* BLDC motor
With
ferromagnetic
rotor:
* SRM
More elaborate
electronics
Three-phase
motors:
* SCIM3, 8
* WRIM4, 7, 8
AC motors:10
* Capacitor
* Resistance
* Split
* Shaded-pole
Three-phase
motors:
* WRSM
* PMSM or
BLAC motor
- IPMSM
- SPMSM
* Hybrid
AC motors:10
* Permanent-split
capacitor
* Hysteresis
* Stepper
* SyRM
* SyRM-PM hybrid
Most elaborate
electronics (VFD), when provided
Notes:
1.
2.
3.
4.
5.
6.
Rotation is independent of the frequency of the AC voltage.
Rotation is equal to synchronous speed (motor stator field speed).
In SCIM fixed-speed operation rotation is equal to slip speed (synchronous speed less slip).
In non-slip energy recovery systems WRIM is usually used for motor starting but can be used to vary load speed.
Variable-speed operation.
Whereas induction and synchronous motor drives are typically with either six-step or sinusoidal waveform
output, BLDC motor drives are usually with trapezoidal current waveform; the behavior of both sinusoidal and
trapezoidal PM machines is however identical in terms of their fundamental aspects.
7. In variable-speed operation WRIM is used in slip energy recovery and double-fed induction machine
applications.
8. Cage winding refers to shorted-circuited squirrel-cage rotor, wound winding being connected externally through
slip rings.
9. Mostly single-phase with some three-phase.
Abbreviations:
•
•
•
•
•
•
•
•
•
•
•
•
•
•
BLAC - Brushless AC
BLDC - Brushless DC
BLDM - Brushless DC motor
EC - Electronic commutator
PM - Permanent magnet
IPMSM - Interior permanent magnet synchronous motor
PMSM - Permanent magnet synchronous motor
SPMSM - Surface permanent magnet synchronous motor
SCIM - Squirrel-cage induction motor
SRM - Switched reluctance motor
SyRM - Synchronous reluctance motor
VFD - Variable-frequency drive
WRIM - Wound-rotor induction motor
WRSM - Wound-rotor synchronous motor
Electric motor
Self-commutated motor
Brushed DC motor
All self-commutated DC motors are by definition run on DC electric power. Most DC motors are small PM types.
They contain a brushed internal mechanical commutation to reverse motor windings' current in synchronism with
rotation.
Electrically excited DC motor
A commutated DC motor has a set of rotating windings wound on
an armature mounted on a rotating shaft. The shaft also carries the
commutator, a long-lasting rotary electrical switch that
periodically reverses the flow of current in the rotor windings as
the shaft rotates. Thus, every brushed DC motor has AC flowing
through its rotating windings. Current flows through one or more
pairs of brushes that bear on the commutator; the brushes connect
an external source of electric power to the rotating armature.
The rotating armature consists of one or more coils of wire wound
around a laminated, magnetically "soft" ferromagnetic core.
Current from the brushes flows through the commutator and one
winding of the armature, making it a temporary magnet (an
electromagnet). The magnets field produced by the armature
Workings of a brushed electric motor with a two-pole
rotor and PM stator. ("N" and "S" designate polarities
interacts with a stationary magnetic field produced by either PMs
on the inside faces of the magnets; the outside faces
or another winding a field coil, as part of the motor frame. The
have opposite polarities.)
force between the two magnetic fields tends to rotate the motor
shaft. The commutator switches power to the coils as the rotor
turns, keeping the magnetic poles of the rotor from ever fully aligning with the magnetic poles of the stator field, so
that the rotor never stops (like a compass needle does), but rather keeps rotating as long as power is applied.
Many of the limitations of the classic commutator DC motor are due to the need for brushes to press against the
commutator. This creates friction. Sparks are created by the brushes making and breaking circuits through the rotor
coils as the brushes cross the insulating gaps between commutator sections. Depending on the commutator design,
this may include the brushes shorting together adjacent sections – and hence coil ends – momentarily while crossing
the gaps. Furthermore, the inductance of the rotor coils causes the voltage across each to rise when its circuit is
opened, increasing the sparking of the brushes. This sparking limits the maximum speed of the machine, as too-rapid
sparking will overheat, erode, or even melt the commutator. The current density per unit area of the brushes, in
combination with their resistivity, limits the output of the motor. The making and breaking of electric contact also
generates electrical noise; sparking generates RFI. Brushes eventually wear out and require replacement, and the
commutator itself is subject to wear and maintenance (on larger motors) or replacement (on small motors). The
commutator assembly on a large motor is a costly element, requiring precision assembly of many parts. On small
motors, the commutator is usually permanently integrated into the rotor, so replacing it usually requires replacing the
whole rotor.
While most commutators are cylindrical, some are flat discs consisting of several segments (typically, at least three)
mounted on an insulator.
Large brushes are desired for a larger brush contact area to maximize motor output, but small brushes are desired for
low mass to maximize the speed at which the motor can run without the brushes excessively bouncing and sparking.
(Small brushes are also desirable for lower cost.) Stiffer brush springs can also be used to make brushes of a given
25
Electric motor
26
mass work at a higher speed, but at the cost of greater friction losses (lower efficiency) and accelerated brush and
commutator wear. Therefore, DC motor brush design entails a trade-off between output power, speed, and
efficiency/wear.
DC machines are defined as follows:
• Armature circuit - A winding where the load current is carried, such that can be either stationary or rotating part
of motor or generator.
• Field circuit - A set of windings that produces a magnetic field so that the electromagnetic induction can take
place in electric machines.
• Commutation: A mechanical technique in which rectification can be achieved, or from which DC can be derived,
in DC machines.
There are five types of brushed DC motor:
• DC shunt-wound motor
• DC series-wound motor
• DC compound motor (two
configurations):
• Cumulative compound
• Differentially compounded
• PM DC motor (not shown)
• Separately excited (not shown).
A: shunt B: series C: compound f = field coil
Permanent magnet DC motor
A PM motor does not have a field winding on the stator frame, instead relying on PMs to provide the magnetic field
against which the rotor field interacts to produce torque. Compensating windings in series with the armature may be
used on large motors to improve commutation under load. Because this field is fixed, it cannot be adjusted for speed
control. PM fields (stators) are convenient in miniature motors to eliminate the power consumption of the field
winding. Most larger DC motors are of the "dynamo" type, which have stator windings. Historically, PMs could not
be made to retain high flux if they were disassembled; field windings were more practical to obtain the needed
amount of flux. However, large PMs are costly, as well as dangerous and difficult to assemble; this favors wound
fields for large machines.
To minimize overall weight and size, miniature PM motors may use high energy magnets made with neodymium or
other strategic elements; most such are neodymium-iron-boron alloy. With their higher flux density, electric
machines with high-energy PMs are at least competitive with all optimally designed singly fed synchronous and
induction electric machines. Miniature motors resemble the structure in the illustration, except that they have at least
three rotor poles (to ensure starting, regardless of rotor position) and their outer housing is a steel tube that
magnetically links the exteriors of the curved field magnets.
Electric motor
Electronic commutator (EC) motor
Brushless DC motor
Some of the problems of the brushed DC motor are eliminated in the BLDC design. In this motor, the mechanical
"rotating switch" or commutator is replaced by an external electronic switch synchronised to the rotor's position.
BLDC motors are typically 85–90% efficient or more. Efficiency for a BLDC motor of up to 96.5% have been
reported, whereas DC motors with brushgear are typically 75–80% efficient.
The BLDC motor's characteristic trapezoidal back-emf waveform is derived partly from stator the stator windings
being evenly distributed, partly from the placement of the rotor's PMs. Also known as electronically commutated DC
or inside out DC motors, the stator windings of trapezoidal BLDC motors can be with single-phase, two-phase or
three-phase and use Hall effect sensors mounted on their windings for rotor position sensing and crude, low cost
closed-loop control of the electronic commutator.
BLDC motors are commonly used where precise speed control is necessary, as in computer disk drives or in video
cassette recorders, the spindles within CD, CD-ROM (etc.) drives, and mechanisms within office products such as
fans, laser printers and photocopiers. They have several advantages over conventional motors:
• Compared to AC fans using shaded-pole motors, they are very efficient, running much cooler than the equivalent
AC motors. This cool operation leads to much-improved life of the fan's bearings.
• Without a commutator to wear out, the life of a BLDC motor can be significantly longer compared to a DC motor
using brushes and a commutator. Commutation also tends to cause a great deal of electrical and RF noise; without
a commutator or brushes, a BLDC motor may be used in electrically sensitive devices like audio equipment or
computers.
• The same Hall effect sensors that provide the commutation can also provide a convenient tachometer signal for
closed-loop control (servo-controlled) applications. In fans, the tachometer signal can be used to derive a "fan
OK" signal as well as provide running speed feedback.
• The motor can be easily synchronized to an internal or external clock, leading to precise speed control.
• BLDC motors have no chance of sparking, unlike brushed motors, making them better suited to environments
with volatile chemicals and fuels. Also, sparking generates ozone which can accumulate in poorly ventilated
buildings risking harm to occupants' health.
• BLDC motors are usually used in small equipment such as computers and are generally used in fans to get rid of
unwanted heat.
• They are also acoustically very quiet motors which is an advantage if being used in equipment that is affected by
vibrations.
Modern BLDC motors range in power from a fraction of a watt to many kilowatts. Larger BLDC motors up to about
100 kW rating are used in electric vehicles. They also find significant use in high-performance electric model
aircraft.
27
Electric motor
28
Switched reluctance motor
The SRM has no brushes or PMs, and the rotor has no electric currents.
Instead, torque comes from a slight misalignment of poles on the rotor
with poles on the stator. The rotor aligns itself with the magnetic field
of the stator, while the stator field stator windings are sequentially
energized to rotate the stator field.
The magnetic flux created by the field windings follows the path of
least magnetic reluctance, meaning the flux will flow through poles of
the rotor that are closest to the energized poles of the stator, thereby
magnetizing those poles of the rotor and creating torque. As the rotor
turns, different windings will be energized, keeping the rotor turning.
SRMs are now being used in some appliances.
6/4 pole switched reluctance motor
Universal AC-DC motor
A commutated series-wound motor is referred to as a universal motor
because it can be designed to operate on either AC or DC power. A
universal motor can operate well on AC because the current in both the
field and the armature (and hence the resultant magnetic fields) will
alternate (reverse polarity) in synchronism, and hence the resulting
mechanical force will occur in a constant direction of rotation.
Operating at normal power line frequencies, universal motors are often
found in a range less than 1000 watts. Universal motors also formed
the basis of the traditional railway traction motor in electric railways.
In this application, the use of AC to power a motor originally designed
to run on DC would lead to efficiency losses due to eddy current
heating of their magnetic components, particularly the motor field
pole-pieces that, for DC, would have used solid (un-laminated) iron
and they are now rarely used.
Modern low-cost universal motor, from a vacuum
cleaner. Field windings are dark copper-colored,
toward the back, on both sides. The rotor's
laminated core is gray metallic, with dark slots
for winding the coils. The commutator (partly
hidden) has become dark from use; it is toward
the front. The large brown molded-plastic piece
in the foreground supports the brush guides and
brushes (both sides), as well as the front motor
bearing.
An advantage of the universal motor is that AC supplies may be used
on motors which have some characteristics more common in DC
motors, specifically high starting torque and very compact design if
high running speeds are used. The negative aspect is the maintenance and short life problems caused by the
commutator. Such motors are used in devices such as food mixers and power tools which are used only
intermittently, and often have high starting-torque demands. Multiple taps on the field coil provide (imprecise)
stepped speed control. Household blenders that advertise many speeds frequently combine a field coil with several
taps and a diode that can be inserted in series with the motor (causing the motor to run on half-wave rectified AC).
Universal motors also lend themselves to electronic speed control and, as such, are an ideal choice for devices like
domestic washing machines. The motor can be used to agitate the drum (both forwards and in reverse) by switching
the field winding with respect to the armature.
Whereas SCIMs cannot turn a shaft faster than allowed by the power line frequency, universal motors can run at
much higher speeds. This makes them useful for appliances such as blenders, vacuum cleaners, and hair dryers
where high speed and light weight are desirable. They are also commonly used in portable power tools, such as
drills, sanders, circular and jig saws, where the motor's characteristics work well. Many vacuum cleaner and weed
Electric motor
trimmer motors exceed 10,000 rpm, while many similar miniature grinders exceed 30,000 rpm.
Externally commutated AC machine
The design of AC induction and synchronous motors is optimized for operation on single-phase or polyphase
sinusoidal or quasi-sinusoidal waveform power such as supplied for fixed-speed application from the AC power grid
or for variable-speed application from VFD controllers. An AC motor has two parts: a stationary stator having coils
supplied with AC to produce a rotating magnetic field, and a rotor attached to the output shaft that is given a torque
by the rotating field.
Induction motor
Cage and wound rotor induction motor
An induction motor is an asynchronous AC motor where power is transferred to the rotor by electromagnetic
induction, much like transformer action. An induction motor resembles a rotating transformer, because the stator
(stationary part) is essentially the primary side of the transformer and the rotor (rotating part) is the secondary side.
Polyphase induction motors are widely used in industry.
Induction motors may be further divided into SCIMs and WRIMs. SCIMs have a heavy winding made up of solid
bars, usually aluminum or copper, joined by rings at the ends of the rotor. When one considers only the bars and
rings as a whole, they are much like an animal's rotating exercise cage, hence the name.
Currents induced into this winding provide the rotor magnetic field. The shape of the rotor bars determines the
speed-torque characteristics. At low speeds, the current induced in the squirrel cage is nearly at line frequency and
tends to be in the outer parts of the rotor cage. As the motor accelerates, the slip frequency becomes lower, and more
current is in the interior of the winding. By shaping the bars to change the resistance of the winding portions in the
interior and outer parts of the cage, effectively a variable resistance is inserted in the rotor circuit. However, the
majority of such motors have uniform bars.
In a WRIM, the rotor winding is made of many turns of insulated wire and is connected to slip rings on the motor
shaft. An external resistor or other control devices can be connected in the rotor circuit. Resistors allow control of the
motor speed, although significant power is dissipated in the external resistance. A converter can be fed from the rotor
circuit and return the slip-frequency power that would otherwise be wasted back into the power system through an
inverter or separate motor-generator.
The WRIM is used primarily to start a high inertia load or a load that requires a very high starting torque across the
full speed range. By correctly selecting the resistors used in the secondary resistance or slip ring starter, the motor is
able to produce maximum torque at a relatively low supply current from zero speed to full speed. This type of motor
also offers controllable speed.
Motor speed can be changed because the torque curve of the motor is effectively modified by the amount of
resistance connected to the rotor circuit. Increasing the value of resistance will move the speed of maximum torque
down. If the resistance connected to the rotor is increased beyond the point where the maximum torque occurs at
zero speed, the torque will be further reduced.
When used with a load that has a torque curve that increases with speed, the motor will operate at the speed where
the torque developed by the motor is equal to the load torque. Reducing the load will cause the motor to speed up,
and increasing the load will cause the motor to slow down until the load and motor torque are equal. Operated in this
manner, the slip losses are dissipated in the secondary resistors and can be very significant. The speed regulation and
net efficiency is also very poor.
29
Electric motor
Torque motor
A torque motor is a specialized form of electric motor which can operate indefinitely while stalled, that is, with the
rotor blocked from turning, without incurring damage. In this mode of operation, the motor will apply a steady
torque to the load (hence the name).
A common application of a torque motor would be the supply- and take-up reel motors in a tape drive. In this
application, driven from a low voltage, the characteristics of these motors allow a relatively constant light tension to
be applied to the tape whether or not the capstan is feeding tape past the tape heads. Driven from a higher voltage,
(and so delivering a higher torque), the torque motors can also achieve fast-forward and rewind operation without
requiring any additional mechanics such as gears or clutches. In the computer gaming world, torque motors are used
in force feedback steering wheels.
Another common application is the control of the throttle of an internal combustion engine in conjunction with an
electronic governor. In this usage, the motor works against a return spring to move the throttle in accordance with the
output of the governor. The latter monitors engine speed by counting electrical pulses from the ignition system or
from a magnetic pickup and, depending on the speed, makes small adjustments to the amount of current applied to
the motor. If the engine starts to slow down relative to the desired speed, the current will be increased, the motor will
develop more torque, pulling against the return spring and opening the throttle. Should the engine run too fast, the
governor will reduce the current being applied to the motor, causing the return spring to pull back and close the
throttle.
Synchronous motor
A synchronous electric motor is an AC motor distinguished by a rotor spinning with coils passing magnets at the
same rate as the AC and resulting magnetic field which drives it. Another way of saying this is that it has zero slip
under usual operating conditions. Contrast this with an induction motor, which must slip to produce torque. One type
of synchronous motor is like an induction motor except the rotor is excited by a DC field. Slip rings and brushes are
used to conduct current to the rotor. The rotor poles connect to each other and move at the same speed hence the
name synchronous motor. Another type, for low load torque, has flats ground onto a conventional squirrel-cage rotor
to create discrete poles. Yet another, such as made by Hammond for its pre-World War II clocks, and in the older
Hammond organs, has no rotor windings and discrete poles. It is not self-starting. The clock requires manual starting
by a small knob on the back, while the older Hammond organs had an auxiliary starting motor connected by a
spring-loaded manually operated switch.
Finally, hysteresis synchronous motors typically are (essentially) two-phase motors with a phase-shifting capacitor
for one phase. They start like induction motors, but when slip rate decreases sufficiently, the rotor (a smooth
cylinder) becomes temporarily magnetized. Its distributed poles make it act like a PMSM. The rotor material, like
that of a common nail, will stay magnetized, but can also be demagnetized with little difficulty. Once running, the
rotor poles stay in place; they do not drift.
Low-power synchronous timing motors (such as those for traditional electric clocks) may have multi-pole PM
external cup rotors, and use shading coils to provide starting torque. Telechron clock motors have shaded poles for
starting torque, and a two-spoke ring rotor that performs like a discrete two-pole rotor.
Doubly fed electric machine
Doubly fed electric motors have two independent multiphase winding sets, which contribute active (i.e., working)
power to the energy conversion process, with at least one of the winding sets electronically controlled for variable
speed operation. Two independent multiphase winding sets (i.e., dual armature) are the maximum provided in a
single package without topology duplication. Doubly fed electric motors are machines with an effective constant
torque speed range that is twice synchronous speed for a given frequency of excitation. This is twice the constant
torque speed range as singly fed electric machines, which have only one active winding set.
30
Electric motor
A doubly fed motor allows for a smaller electronic converter but the cost of the rotor winding and slip rings may
offset the saving in the power electronics components. Difficulties with controlling speed near synchronous speed
limit applications.
Special magnetic motors
Rotary
Hydraulic cylinder displacement
Electric motors are replacing hydraulic cylinders in airplanes and military equipment.
Ironless or coreless rotor motor
Nothing in the principle of any of the motors described above requires
that the iron (steel) portions of the rotor actually rotate. If the soft
magnetic material of the rotor is made in the form of a cylinder, then
(except for the effect of hysteresis) torque is exerted only on the
windings of the electromagnets. Taking advantage of this fact is the
coreless or ironless DC motor, a specialized form of a PM DC motor.
Optimized for rapid acceleration, these motors have a rotor that is
constructed without any iron core. The rotor can take the form of a
winding-filled cylinder, or a self-supporting structure comprising only
the magnet wire and the bonding material. The rotor can fit inside the
A Miniature Coreless Motor
stator magnets; a magnetically soft stationary cylinder inside the rotor
provides a return path for the stator magnetic flux. A second arrangement has the rotor winding basket surrounding
the stator magnets. In that design, the rotor fits inside a magnetically soft cylinder that can serve as the housing for
the motor, and likewise provides a return path for the flux.
Because the rotor is much lighter in weight (mass) than a conventional rotor formed from copper windings on steel
laminations, the rotor can accelerate much more rapidly, often achieving a mechanical time constant under one ms.
This is especially true if the windings use aluminum rather than the heavier copper. But because there is no metal
mass in the rotor to act as a heat sink, even small coreless motors must often be cooled by forced air. Overheating
might be an issue for coreless DC motor designs.
Among these types are the disc-rotor types, described in more detail in the next section.
Vibrator motors for cellular phones are sometimes tiny cylindrical PM field types, but there are also disc-shaped
types which have a thin multipolar disc field magnet, and an intentionally unbalanced molded-plastic rotor structure
with two bonded coreless coils. Metal brushes and a flat commutator switch power to the rotor coils.
Related limited-travel actuators have no core and a bonded coil placed between the poles of high-flux thin PMs.
These are the fast head positioners for rigid-disk ("hard disk") drives. Although the contemporary design differs
considerably from that of loudspeakers, it is still loosely (and incorrectly) referred to as a "voice coil" structure,
because some earlier rigid-disk-drive heads moved in straight lines, and had a drive structure much like that of a
loudspeaker.
31
Electric motor
Pancake or axial rotor motor
A rather unusual motor design, the printed armature or pancake motor has the windings shaped as a disc running
between arrays of high-flux magnets. The magnets are arranged in a circle facing the rotor with space in between to
form an axial air gap. This design is commonly known as the pancake motor because of its extremely flat profile,
although the technology has had many brand names since its inception, such as ServoDisc.
The printed armature (originally formed on a printed circuit board) in a printed armature motor is made from
punched copper sheets that are laminated together using advanced composites to form a thin rigid disc. The printed
armature has a unique construction in the brushed motor world in that it does not have a separate ring commutator.
The brushes run directly on the armature surface making the whole design very compact.
An alternative manufacturing method is to use wound copper wire laid flat with a central conventional commutator,
in a flower and petal shape. The windings are typically stabilized by being impregnated with electrical epoxy potting
systems. These are filled epoxies that have moderate mixed viscosity and a long gel time. They are highlighted by
low shrinkage and low exotherm, and are typically UL 1446 recognized as a potting compound insulated with
180°C, Class H rating.
The unique advantage of ironless DC motors is that there is no cogging (torque variations caused by changing
attraction between the iron and the magnets). Parasitic eddy currents cannot form in the rotor as it is totally ironless,
although iron rotors are laminated. This can greatly improve efficiency, but variable-speed controllers must use a
higher switching rate (>40 kHz) or DC because of the decreased electromagnetic induction.
These motors were originally invented to drive the capstan(s) of magnetic tape drives in the burgeoning computer
industry, where minimal time to reach operating speed and minimal stopping distance were critical. Pancake motors
are still widely used in high-performance servo-controlled systems, robotic systems, industrial automation and
medical devices. Due to the variety of constructions now available, the technology is used in applications from high
temperature military to low cost pump and basic servos.
Servo motor
A servomotor is a motor, very often sold as a complete module, which is used within a position-control or
speed-control feedback control system mainly control valves, such as motor operated control valves. Servomotors
are used in applications such as machine tools, pen plotters, and other process systems. Motors intended for use in a
servomechanism must have well-documented characteristics for speed, torque, and power. The speed vs. torque
curve is quite important and is high ratio for a servo motor. Dynamic response characteristics such as winding
inductance and rotor inertia are also important; these factors limit the overall performance of the servomechanism
loop. Large, powerful, but slow-responding servo loops may use conventional AC or DC motors and drive systems
with position or speed feedback on the motor. As dynamic response requirements increase, more specialized motor
designs such as coreless motors are used. AC motors' superior power density and acceleration characteristics
compared to that of DC motors tends to favor PM synchronous, BLDC, induction, and SRM drive applications.
A servo system differs from some stepper motor applications in that the position feedback is continuous while the
motor is running; a stepper system relies on the motor not to "miss steps" for short term accuracy, although a stepper
system may include a "home" switch or other element to provide long-term stability of control. For instance, when a
typical dot matrix computer printer starts up, its controller makes the print head stepper motor drive to its left-hand
limit, where a position sensor defines home position and stops stepping. As long as power is on, a bidirectional
counter in the printer's microprocessor keeps track of print-head position.
32
Electric motor
33
Stepper motor
Stepper motors are a type of motor frequently used when precise
rotations are required. In a stepper motor an internal rotor containing
PMs or a magnetically soft rotor with salient poles is controlled by a
set of external magnets that are switched electronically. A stepper
motor may also be thought of as a cross between a DC electric motor
and a rotary solenoid. As each coil is energized in turn, the rotor aligns
itself with the magnetic field produced by the energized field winding.
Unlike a synchronous motor, in its application, the stepper motor may
not rotate continuously; instead, it "steps"—starts and then quickly
stops again—from one position to the next as field windings are
energized and de-energized in sequence. Depending on the sequence,
the rotor may turn forwards or backwards, and it may change direction,
stop, speed up or slow down arbitrarily at any time.
A stepper motor with a soft iron rotor, with active
windings shown. In 'A' the active windings tend
to hold the rotor in position. In 'B' a different set
of windings are carrying a current, which
generates torque and rotation.
Simple stepper motor drivers entirely energize or entirely de-energize the field windings, leading the rotor to "cog"
to a limited number of positions; more sophisticated drivers can proportionally control the power to the field
windings, allowing the rotors to position between the cog points and thereby rotate extremely smoothly. This mode
of operation is often called microstepping. Computer controlled stepper motors are one of the most versatile forms of
positioning systems, particularly when part of a digital servo-controlled system.
Stepper motors can be rotated to a specific angle in discrete steps with ease, and hence stepper motors are used for
read/write head positioning in computer floppy diskette drives. They were used for the same purpose in pre-gigabyte
era computer disk drives, where the precision and speed they offered was adequate for the correct positioning of the
read/write head of a hard disk drive. As drive density increased, the precision and speed limitations of stepper motors
made them obsolete for hard drives—the precision limitation made them unusable, and the speed limitation made
them uncompetitive—thus newer hard disk drives use voice coil-based head actuator systems. (The term "voice coil"
in this connection is historic; it refers to the structure in a typical (cone type) loudspeaker. This structure was used
for a while to position the heads. Modern drives have a pivoted coil mount; the coil swings back and forth,
something like a blade of a rotating fan. Nevertheless, like a voice coil, modern actuator coil conductors (the magnet
wire) move perpendicular to the magnetic lines of force.)
Stepper motors were and still are often used in computer printers, optical scanners, and digital photocopiers to move
the optical scanning element, the print head carriage (of dot matrix and inkjet printers), and the platen or feed rollers.
Likewise, many computer plotters (which since the early 1990s have been replaced with large-format inkjet and laser
printers) used rotary stepper motors for pen and platen movement; the typical alternatives here were either linear
stepper motors or servomotors with closed-loop analog control systems.
So-called quartz analog wristwatches contain the smallest commonplace stepping motors; they have one coil, draw
very little power, and have a PM rotor. The same kind of motor drives battery-powered quartz clocks. Some of these
watches, such as chronographs, contain more than one stepping motor.
Closely related in design to three-phase AC synchronous motors, stepper motors and SRMs are classified as variable
reluctance motor type.[7] Stepper motors were and still are often used in computer printers, optical scanners, and
computer numerical control (CNC) machines such as routers, plasma cutters and CNC lathes.
Electric motor
34
Linear motor
A linear motor is essentially any electric motor that has been "unrolled" so that, instead of producing a torque
(rotation), it produces a straight-line force along its length.
Linear motors are most commonly induction motors or stepper motors. Linear motors are commonly found in many
roller-coasters where the rapid motion of the motorless railcar is controlled by the rail. They are also used in maglev
trains, where the train "flies" over the ground. On a smaller scale, the 1978 era HP 7225A pen plotter used two linear
stepper motors to move the pen along the X and Y axes.
Comparison by major categories
Comparison of motor types
Type
Advantages
Disadvantages
Typical application
Typical drive, output
Self-commutated motors
Brushed DC
Simple speed control
Maintenance (brushes)
Medium lifespan
Costly commutator and
brushes
Steel mills
Paper making machines
Treadmill exercisers
Automotive accessories
Rectifier, linear transistor(s) or DC
[8]
chopper controller.
Brushless
DC motor
(BLDC)
or
(BLDM)
Long lifespan
Low maintenance
High efficiency
Higher initial cost
Requires EC controller
with closed-loop control
Rigid ("hard") disk drives
CD/DVD players
Electric vehicles
RC Vehicles
UAVs
Synchronous; single-phase or
three-phase with PM rotor and
trapezoidal stator winding; VFD
typically VS PWM inverter type.
Switched
reluctance
motor
(SRM)
Long lifespan
Low maintenance
High efficiency
No permanent magnets
Low cost
Simple construction
Mechanical resonance
possible
High iron losses
Not possible:
* Open or vector control
* Parallel operation
[7]
Requires EC controller
Appliances
Electric Vehicles
Textile mills
Aircraft applications
PWM and various other drive types,
which tend to be used in very
specialized / OEM applications.
Universal
motor
High starting torque,
compact, high speed.
Maintenance (brushes)
Handheld power tools, blenders,
Shorter lifespan
vacuum cleaners, insulation
Usually acoustically noisy blowers
Only small ratings are
economical
Variable single phase AC, half-wave or
full-wave phase-angle control with
triac(s); closed-loop control optional.
AC asynchronous motors
AC polyphase
squirrel-cage
or
wound-rotor
induction motor
(SCIM)
or
(WRIM)
Self-starting
Low cost
Robust
Reliable
Ratings to 1+ MW
Standardized types.
High starting current
Lower efficiency
due to need
for magnetization.
Fixed-speed, traditionally, SCIM
the world's workhorse especially
in low performance applications
of all types
Variable-speed, traditionally,
low-performance
variable-torque pumps, fans,
blowers and compressors.
Variable-speed, increasingly,
other high-performance
constant-torque and
constant-power or dynamic
loads.
Fixed-speed, low performance
applications of all types.
Variable-speed, traditionally, WRIM
drives or fixed-speed V/Hz-controlled
VSDs.
Variable-speed, increasingly,
vector-controlled VSDs displacing DC,
WRIM and single-phase AC induction
motor drives.
Electric motor
35
AC SCIM
split-phase
capacitor-start
High power
high starting torque
Speed slightly below
synchronous
Starting switch or relay
required
Appliances
Stationary Power Tools
AC SCIM
split-phase
capacitor-run
Moderate power
High starting torque
No starting switch
Comparatively long life
Speed slightly below
synchronous
Slightly more costly
Industrial blowers
Industrial machinery
AC SCIM
split-phase,
auxiliary
start winding
Moderate power
Low starting torque
Speed slightly below
synchronous
Starting switch or relay
required
Appliances
Stationary power tools
AC induction
shaded-pole
motor
Low cost
Long life
Speed slightly below
synchronous
Low starting torque
Small ratings
low efficiency
Fans, appliances, record players
Fixed or variable single-phase AC,
variable speed being derived, typically,
by full-wave phase-angle control with
triac(s); closed-loop control
[8]
optional.
AC synchronous motors
Wound-rotor
synchronous
motor
(WRSM)
Synchronous speed
Inherently
more efficient
induction motor,
low power factor
More costly
Industrial motors
Fixed or variable speed, three-phase;
VFD typically six-step CS
load-commutated inverter type or VS
[]
PWM inverter type.
Hysteresis
motor
Accurate speed control
Low noise
No vibration
High starting
torque
Very low efficiency
Clocks, timers, sound producing
or recording equipment, hard
drive, capstan drive
Single-phase AC, two-phase
capacitor-start, capacitor run motor
Synchronous
reluctance
motor
(SyRM)
Equivalent to SCIM
except more robust,
more efficient, runs
cooler, smaller footprint
Competes with PM
synchronous motor
without demagnetization
issues
Requires a controller
Not widely available
High cost
Appliances
Electric vehicles
Textile mills
Aircraft applications
VFD can be standard DTC type or VS
inverter PWM type.
Speciality motors
Pancake
or axial
rotor
motors
Compact design
Simple speed control
Medium cost
Medium lifespan
Office Equip
Fans/Pumps, fast industrial and
military servos
Drives can typically be brushed or
brushless DC type.
Stepper
motor
Precision positioning
High holding torque
Some can be costly
Require a controller
Positioning in printers and
floppy disc drives; industrial
machine tools
Not a VFD. Stepper position is
[9][10]
determined by pulse counting.
Electric motor
36
Electromagnetism
Force and torque
The fundamental purpose of the vast majority of the world's electric motors is to electromagnetically induce relative
movement in an air gap between a stator and rotor to produce useful torque or linear force.
According Lorentz force law the force of a winding conductor can be given simply by:
or more generally, to handle conductors with any geometry:
The most general approaches to calculating the forces in motors use tensors.
Power
Where rpm is shaft speed and T is torque, a motor's mechanical power output Pem is given by,
in British units with T expressed in foot-pounds,
(horsepower), and,
in SI units with shaft speed expressed in radians per second, and T expressed in newton-meters,
(watts).
For a linear motor, with force F and velocity v expressed in newtons and meters per second,
(watts).
In an asynchronous or induction motor, the relationship between motor speed and air gap power is, neglecting skin
effect, given by the following:
, where
Rr - rotor resistance
Ir2 - square of current induced in the rotor
s - motor slip; ie, difference between synchronous speed and slip speed, which provides the relative
movement needed for current induction in the rotor.
Back emf
Since the armature windings of a direct-current motor are moving through a magnetic field, they have a voltage
induced in them. This voltage tends to oppose the motor supply voltage and so is called "back electromotive force
(emf)". The voltage is proportional to the running speed of the motor. The back emf of the motor, plus the voltage
drop across the winding internal resistance and brushes, must equal the voltage at the brushes. This provides the
fundamental mechanism of speed regulation in a DC motor. If the mechanical load increases, the motor slows down;
a lower back emf results, and more current is drawn from the supply. This increased current provides the additional
torque to balance the new load.
In AC machines, it is sometimes useful to consider a back emf source within the machine; this is of particular
concern for close speed regulation of induction motors on VFDs, for example.
Electric motor
37
Losses
Motor losses are mainly due to resistive losses in windings, core losses and mechanical losses in bearings, and
aerodynamic losses, particularly where cooling fans are present, also occur.
Losses also occur in commutation, mechanical commutators spark, and electronic commutators and also dissipate
heat.
Efficiency
To calculate a motor's efficiency, the mechanical output power is divided by the electrical input power:
,
where
is energy conversion efficiency,
where
is input voltage,
is electrical input power, and
is input current,
is output torque, and
is mechanical output power:
is output angular velocity. It is possible to
derive analytically the point of maximum efficiency. It is typically at less than 1/2 the stall torque.[citation needed]
Various regulatory authorities in many countries have introduced and implemented legislation to encourage the
manufacture and use of higher efficiency electric motors. There is existing and forthcoming legislation regarding the
future mandatory use of premium-efficiency induction-type motors in defined equipment. For more information,
see: Premium efficiency and Copper in energy efficient motors.
Goodness factor
Professor
Eric
Laithwaite
proposed
a
metric
to
determine
the
'goodness'
of
an
electric
motor:
Where:
is the goodness factor (factors above 1 are likely to be efficient)
are the cross sections of the magnetic and electric circuit
are the lengths of the magnetic and electric circuits
is the permeability of the core
is the angular frequency the motor is driven at
From this, he showed that the most efficient motors are likely to have relatively large magnetic poles. However, the
equation only directly relates to non PM motors.
Performance parameters
Torque capability of motor types
When optimally designed within a given core saturation constraint and for a given active current (i.e., torque
current), voltage, pole-pair number, excitation frequency (i.e., synchronous speed), and air-gap flux density, all
categories of electric motors or generators will exhibit virtually the same maximum continuous shaft torque (i.e.,
operating torque) within a given air-gap area with winding slots and back-iron depth, which determines the physical
size of electromagnetic core. Some applications require bursts of torque beyond the maximum operating torque, such
as short bursts of torque to accelerate an electric vehicle from standstill. Always limited by magnetic core saturation
or safe operating temperature rise and voltage, the capacity for torque bursts beyond the maximum operating torque
Electric motor
differs significantly between categories of electric motors or generators.
Capacity for bursts of torque should not be confused with field weakening capability. Field weakening allows an
electric machine to operate beyond the designed frequency of excitation.
Electric machines without a transformer circuit topology, such as that of WRSMs or PMSMs, cannot realize bursts
of torque higher than the maximum designed torque without saturating the magnetic core and rendering any increase
in current as useless. Furthermore, the PM assembly of PMSMs can be irreparably damaged, if bursts of torque
exceeding the maximum operating torque rating are attempted.
Electric machines with a transformer circuit topology, such as induction machines, induction doubly fed electric
machines, and induction or synchronous wound-rotor doubly fed (WRDF) machines, exhibit very high bursts of
torque because the emf-induced active current on either side of the transformer oppose each other and thus contribute
nothing to the transformer coupled magnetic core flux density, which would otherwise lead to core saturation.
Electric machines that rely on induction or asynchronous principles short-circuit one port of the transformer circuit
and as a result, the reactive impedance of the transformer circuit becomes dominant as slip increases, which limits
the magnitude of active (i.e., real) current. Still, bursts of torque that are two to three times higher than the maximum
design torque are realizable.
The brushless wound-rotor synchonous doubly fed (BWRSDF) machine is the only electric machine with a truly
dual ported transformer circuit topology (i.e., both ports independently excited with no short-circuited port). The
dual ported transformer circuit topology is known to be unstable and requires a multiphase slip-ring-brush assembly
to propagate limited power to the rotor winding set. If a precision means were available to instantaneously control
torque angle and slip for synchronous operation during motoring or generating while simultaneously providing
brushless power to the rotor winding set, the active current of the BWRSDF machine would be independent of the
reactive impedance of the transformer circuit and bursts of torque significantly higher than the maximum operating
torque and far beyond the practical capability of any other type of electric machine would be realizable. Torque
bursts greater than eight times operating torque have been calculated.
Continuous torque density
The continuous torque density of conventional electric machines is determined by the size of the air-gap area and the
back-iron depth, which are determined by the power rating of the armature winding set, the speed of the machine,
and the achievable air-gap flux density before core saturation. Despite the high coercivity of neodymium or
samarium-cobalt PMs, continuous torque density is virtually the same amongst electric machines with optimally
designed armature winding sets. Continuous torque density relates to method of cooling and permissible period of
operation before destruction by overheating of windings or PM damage.
Continuous power density
The continuous power density is determined by the product of the continuous torque density and the constant torque
speed range of the electric machine.
Standards
The following are major design and manufacturing standards covering electric motors:
• International Electrotechnical Commission: IEC 60034 Rotating Electrical Machines
• National Electrical Manufacturers Association: MG-1 Motors and Generators [11]
• Underwriters Laboratories: UL 1004 - Standard for Electric Motors
38
Electric motor
Non-magnetic motors
An electrostatic motor is based on the attraction and repulsion of electric charge. Usually, electrostatic motors are the
dual of conventional coil-based motors. They typically require a high voltage power supply, although very small
motors employ lower voltages. Conventional electric motors instead employ magnetic attraction and repulsion, and
require high current at low voltages. In the 1750s, the first electrostatic motors were developed by Benjamin Franklin
and Andrew Gordon. Today the electrostatic motor finds frequent use in micro-electro-mechanical systems (MEMS)
where their drive voltages are below 100 volts, and where moving, charged plates are far easier to fabricate than
coils and iron cores. Also, the molecular machinery which runs living cells is often based on linear and rotary
electrostatic motors. [citation needed]
A piezoelectric motor or piezo motor is a type of electric motor based upon the change in shape of a piezoelectric
material when an electric field is applied. Piezoelectric motors make use of the converse piezoelectric effect whereby
the material produces acoustic or ultrasonic vibrations in order to produce a linear or rotary motion. In one
mechanism, the elongation in a single plane is used to make a series stretches and position holds, similar to the way a
caterpillar moves. [citation needed]
An electrically powered spacecraft propulsion system uses electric motor technology to propel spacecraft in outer
space, most systems being based on electrically powering propellant to high speed, with some systems being based
on electrodynamic tethers principles of propulsion to the magnetosphere.[citation needed]
Notes
[1]
[2]
[3]
[4]
[5]
Tom McInally, The Sixth Scottish University. The Scots Colleges Abroad: 1575 to 1799 (Brill, Leiden, 2012) p. 115
Ganot provides a superb illustration of one such early electric motor designed by Froment. UNIQ-ref-0-1ffedc363aef1e38-QINU
Hameyer, §5.1, p. 62
Lynn, §83, p. 812
The term 'electronic commutator motor' (ECM) is identified with the heating, ventilation and air-conditioning (HVAC) industry, the
distinction between BLDC and BLAC being in this context seen as a function of degree of ECM drive complexity with BLDC drives typically
being with simple single-phase scalar-controlled voltage-regulated trapezoidal current waveform output involving surface PM motor
construction and BLAC drives tending towards more complex three-phase vector-controlled current-regulated sinusoidal waveform involving
interior PM motor construction.
[6] The universal and repulsion motors are part of a class of motors known as AC commutator motors, which also includes the following now
largely obsolete motor types: Single-phase - straight and compensated series motors, railway motor; three-phase - various repulsion motor
types, brush-shifting series motor, brush-shifting polyphase shunt or Schrage motor, Fynn-Weichsel motor.Tom McInally, The Sixth Scottish
University. The Scots Colleges Abroad: 1575 to 1799 (Brill, Leiden, 2012) p. 115
[7] Bose, pp. 569–570, 891
[8] Stölting, p. 9
[9] Stölting, p. 10
[10] Bose, p. 389
[11] http:/ / www. nema. org/ Standards/ Pages/
Information-Guide-for-General-Purpose-Industrial-AC-Small-and-Medium-Squirrel-Cage-Induction-Motor-Standards. aspx
References
Bibliography
• Fink, Donald G.; Beaty, H. Wayne, Standard Handbook for Electrical Engineers, '14th ed., McGraw-Hill, 1999,
ISBN 0-07-022005-0.
• Houston, Edwin J.; Kennelly, Arthur, Recent Types of Dynamo-Electric Machinery (http://archive.org/details/
recenttypesdyna00kenngoog), American Technical Book Company 1897, published by P.F. Collier and Sons New
York, 1902
• Kuphaldt, Tony R. (2000–2006). "Chapter 13 AC MOTORS" (http://www.ibiblio.org/obp/electricCircuits/
AC/AC_13.html). Lessons In Electric Circuits—Volume II. Retrieved 2006-04-11.
39
Electric motor
• Rosenblatt, Jack; Friedman, M. Harold, Direct and Alternating Current Machinery, 2nd ed., McGraw-Hill, 1963
Further reading
• Bedford, B.D.; Hoft, R.G. (1964). Principles of Inverter Circuits (http://books.google.ca/books/about/
Principles_of_inverter_circuits.html?id=iyZTAAAAMAAJ&redir_esc=y). New York: Wiley.
ISBN 0-471-06134-4.
• Bose, Bimal K. (2006). Power Electronics and Motor Drives : Advances and Trends (http://books.google.ca/
books/about/Power_Electronics_And_Motor_Drives.html?id=ywiBVSnYm6IC&redir_esc=y). Academic
Press. ISBN 978-0-12-088405-6.
• Chiasson, John (2005). Modeling and High-Performance Control of Electric Machines (http://books.google.ca/
books?id=cq6RPPsOyt8C&pg=PR14&lpg=PR14&dq=Modeling+and+high-performance+control+of+
electric+machines&source=bl&ots=yBsEH7igCT&sig=DN136AEwyoU8ihl95hxPXco9fQ4&hl=en&sa=X&
ei=DEUwUayWBaK9iwKS5oCgCw&ved=0CDUQ6AEwAQ) (Online ed.). Wiley. ISBN 0-471-68449-X.
Check |isbn= value (help).
• Fitzgerald, A.E.; Kingsley, Charles , Jr.; Umans, Stephen D. (2003). Electric Machinery (http://books.google.
ca/books/about/Electric_Machinery.html?id=YBKk4kWSle0C&redir_esc=y) (6th ed.). McGraw-Hill. pp. 688
pages. ISBN 978-0-07-366009-7. Check |isbn= value (help).
• Pelly, B.R. (1971). Thyristor Phase-Controlled Converters and Cycloconverters : Operation, Control, and
Performance (http://books.google.ca/books/about/Thyristor_phase_controlled_converters_an.
html?id=l9YiAAAAMAAJ&redir_esc=y). Wiley-Interscience. ISBN 978-0-471-67790-1.
• Stölting, H. D.; Kallenbach, E.; Amrhein, W. (eds.) (2008). Handbook of Fractional-Horsepower Drives (http://
books.google.ca/books?id=VCHumncaeAAC&pg=PA134&lpg=PA134&dq=fractional+horsepower+ac+
motor+comparison&source=bl&ots=6uqhzdi1HV&sig=DtSYJ-53rU_IRny-yDui5bv1b8c&hl=en&sa=X&
ei=EUgkUdveN8eRiAL1hICAAg&sqi=2&ved=0CH4Q6AEwCQ#v=onepage&q=shaded-pole&f=false)
(Online ed.). Springer. ISBN 978-3-540-73128-3.
External links
• SparkMuseum: Early Electric Motors (http://www.sparkmuseum.com/MOTORS.HTM)
• The Invention of the Electric Motor 1800 to 1893 (http://www.eti.kit.edu/english/1376.php), hosted by
Karlsrushe Institute of Technology's Martin Doppelbauer
• Electric Motors and Generators (http://www.phys.unsw.edu.au/~jw/HSCmotors.html), a U. of NSW
Physclips multimedia resource
• IEA 4E - Efficient Electrical End-Use Equipment (http://www.iea-4e.org/).
• iPES Rotating Magnetic Field (http://www.ipes.ethz.ch/ipes/2002Feldlinien/feld_dreh.html), animation
40
Tactile sensor
Tactile sensor
The term tactile sensor usually refers to a transducer that is sensitive to touch, force, or pressure.[1] Tactile sensors
are employed wherever interactions between a contact surface and the environment are to be measured and
registered. Tactile sensors are useful in a wide variety of applications for robotics and computer hardware and even
security systems.
A sensor's sensitivity indicates how much the sensor's output changes when the measured quantity changes. The term
tactile refers to the somatosensory system or more commonly the sense of touch. A tactile sensor is a device which
receives and responds to a signal or stimulus having to do with force.
Tactile sensors are generally known and can be grouped into a number of different types depending upon their
construction; the most common groups are piezoresistive, piezoelectric, capacitive and elastoresistive sensors.[2]
Uses
Tactile sensors are often in everyday objects such as elevator buttons and lamps which dim or brighten by touching
the base. There are also innumerable applications for tactile sensors of which most people are never aware.
Sensors that measure very small changes must have very high sensitivities. Sensors need to be designed to have a
small effect on what is measured; making the sensor smaller often improves this and may introduce other
advantages. Tactile sensors can be used to test the performance of all types of applications. For example, these
sensors have been noted to be used in the manufacturing of automobiles (brakes, clutches, door seals, gasket), battery
lamination, bolted joints, fuel cell, etc.
In robots designed to interact with objects requiring handling involving: precision, dexterity, or interaction with
unusual objects it becomes increasingly necessary to provide sensory apparatus which is functionally equivalent to
the various sensors which human workers are naturally endowed. Tactile sensors have been developed for use with
robots. Tactile sensors can complement visual systems by providing information at the time contact is made between
a gripper of the robot and an object being gripped. At this time vision is no longer sufficient, as the mechanical
properties of the object cannot be determined by vision alone. Determining weight, center of mass, coefficient of
friction, and thermal conductivity require object interaction and some sort of tactile sensing. Several classes of tactile
sensors are used in robots:
Pressure Sensor Arrays
Pressure sensor arrays are large grids of tactels. A tactel is a ‘tactile element’. Each tactel is capable of detecting
normal forces. The advantage of tactel based sensors is that they provide a high resolution ‘image’ of the contact
surface. Alongside spatial resolution and force sensitivity, systems-integration questions such as wiring and signal
routing are important.[3] Pressure sensor arrays are often available in thin-film form. They are primarily used as
analytical tools used in the manufacturing and R&D processes by engineers and technician, but have been adapted to
be used in robots. Examples of such sensors available to consumers include arrays built from conductive rubber,[4]
lead zirconate titanate (PZT), polyvinylidene fluoride(PVDF), PVDF-TrFE,[5] FET,[6] and metallic capacitive
sensing elements.
41
Tactile sensor
Strain Gauge Rosettes
Strain gauges rosettes are constructed from multiple strain gauges, where each are responsible for detecting the force
or torque in a particular direction. The advantage of strain gauges is that when the information from each individual
strain gauge is combined, the information allows determination of a pattern of forces or torques.[7]
Biologically Inspired Tactile Sensors
A variety of biologically inspired designs have been suggested,.[8][9][10] One characteristic of biologically inspired
tactile sensors is that they often incorporate more than one sensing strategy. For example, they might detect both the
distribution of pressures, and the pattern of forces that would come from pressure sensor arrays and strain gauge
rosettes. Hence, the advantage of a biologically designed tactile sensor is able to perform multiple types of sensing,
e.g. two-point discrimination and force sensing both with human-like ability.
Advanced versions of biologically designed tactile sensors include vibration sensing which has been determined to
be important for understanding interactions between the tactile sensor and objects where the sensor slides over the
object. Such interactions are now understood to be important for human tool use and judging the texture of an object.
One such sensor combines force sensing, vibration sensing, and heat transfer sensing.[11]
References
[1] Tactile Sensing—From Humans to Humanoids (http:/ / ieeexplore. ieee. org/ xpl/ articleDetails. jsp?tp=& arnumber=5339133&
contentType=Journals+ & + Magazines& sortType=desc_p_Publication_Year& searchField=Search_All& queryText=tactile+ sensing+ -+
from+ humans+ to+ humanoids)
[2] Robotic Tactile Sensing - Technologies and System (http:/ / www. springer. com/ engineering/ robotics/ book/ 978-94-007-0578-4)
[3] Tactile Sensing - From Humans to Humanoids (http:/ / ieeexplore. ieee. org/ xpl/ articleDetails. jsp?arnumber=5339133)
[4] A tactile sensor sheet using pressure conductive rubber with electrical-wires stitched method (http:/ / ieeexplore. ieee. org/ xpl/ freeabs_all.
jsp?arnumber=1331366)
[5] Towards Tactile Sensing System on Chip for Robotic Applications (http:/ / ieeexplore. ieee. org/ xpls/ abs_all. jsp?arnumber=5887375)
[6] Piezoelectric oxide semiconductor field effect transistor touch sensing devices (http:/ / apl. aip. org/ resource/ 1/ applab/ v95/ i3/ p034105_s1)
[7] Data sheet for Schunk FT-Nano 43, a 6-axis force torque sensor (http:/ / www. schunk. com/ schunk_files/ attachments/ FT-Nano_43_EN.
pdf)
[8] A robust micro-vibration sensor for biomimetic fingertips (http:/ / ieeexplore. ieee. org/ xpl/ freeabs_all. jsp?arnumber=4762917)
[9] Development of a tactile sensor based on biologically inspired edge encoding (http:/ / ieeexplore. ieee. org/ xpl/ freeabs_all.
jsp?arnumber=5174720)
[10] A biologically inspired tactile sensor array utilizing phase-based computation (http:/ / ieeexplore. ieee. org/ Xplore/ login. jsp?url=http:/ /
ieeexplore. ieee. org/ iel5/ 4591408/ 4600290/ 04600304. pdf?arnumber=4600304& authDecision=-203)
[11] Syntouch technology (http:/ / www. syntouchllc. com/ Technology/ TechnologyOverview. php)
External links
• Automation and Robotics (http://www.soton.ac.uk/~rmc1/robotics/artactile.htm)
• Tactile/Touch and Resistive Based Sensors (http://library.thinkquest.org/C0126120/tactile.htm)
42
Computer vision
Computer vision
Computer vision is a field that includes methods for acquiring, processing, analyzing, and understanding images
and, in general, high-dimensional data from the real world in order to produce numerical or symbolic information,
e.g., in the forms of decisions. A theme in the development of this field has been to duplicate the abilities of human
vision by electronically perceiving and understanding an image. This image understanding can be seen as the
disentangling of symbolic information from image data using models constructed with the aid of geometry, physics,
statistics, and learning theory. Computer vision has also been described as the enterprise of automating and
integrating a wide range of processes and representations for vision perception.
Applications range from tasks such as industrial machine vision systems which, say, inspect bottles speeding by on a
production line, to research into artificial intelligence and computers or robots that can comprehend the world around
them. The computer vision and machine vision fields have significant overlap. Computer vision covers the core
technology of automated image analysis which is used in many fields. Machine vision usually refers to a process of
combining automated image analysis with other methods and technologies to provide automated inspection and
robot guidance in industrial applications.
As a scientific discipline, computer vision is concerned with the theory behind artificial systems that extract
information from images. The image data can take many forms, such as video sequences, views from multiple
cameras, or multi-dimensional data from a medical scanner.
As a technological discipline, computer vision seeks to apply its theories and models to the construction of computer
vision systems. Examples of applications of computer vision include systems for:
•
•
•
•
•
•
•
Controlling processes, e.g., an industrial robot;
Navigation, e.g., by an autonomous vehicle or mobile robot;
Detecting events, e.g., for visual surveillance or people counting;
Organizing information, e.g., for indexing databases of images and image sequences;
Modeling objects or environments, e.g., medical image analysis or topographical modeling;
Interaction, e.g., as the input to a device for computer-human interaction, and
Automatic inspection, e.g., in manufacturing applications.
Sub-domains of computer vision include scene reconstruction, event detection, video tracking, object recognition,
learning, indexing, motion estimation, and image restoration.
In most practical computer vision applications, the computers are pre-programmed to solve a particular task, but
methods based on learning are now becoming increasingly common.
43
Computer vision
Related fields
Areas of artificial intelligence deal
with
autonomous
planning
or
deliberation for robotical systems to
navigate through an environment. A
detailed understanding of these
environments is required to navigate
through them. Information about the
environment could be provided by a
computer vision system, acting as a
vision sensor and providing high-level
information about the environment and
the robot.
Artificial intelligence and computer
vision share other topics such as
Relation between computer vision and various other fieldsWikipedia:No original research
pattern recognition and learning
techniques. Consequently, computer
vision is sometimes seen as a part of the artificial intelligence field or the computer science field in general.
Solid-state physics is another field that is closely related to computer vision. Most computer vision systems rely on
image sensors, which detect electromagnetic radiation which is typically in the form of either visible or infra-red
light. The sensors are designed using quantum physics. The process by which light interacts with surfaces is
explained using physics. Physics explains the behavior of optics which are a core part of most imaging systems.
Sophisticated image sensors even require quantum mechanics to provide a complete understanding of the image
formation process. Also, various measurement problems in physics can be addressed using computer vision, for
example motion in fluids.
A third field which plays an important role is neurobiology, specifically the study of the biological vision system.
Over the last century, there has been an extensive study of eyes, neurons, and the brain structures devoted to
processing of visual stimuli in both humans and various animals. This has led to a coarse, yet complicated,
description of how "real" vision systems operate in order to solve certain vision related tasks. These results have led
to a subfield within computer vision where artificial systems are designed to mimic the processing and behavior of
biological systems, at different levels of complexity. Also, some of the learning-based methods developed within
computer vision (e.g. neural net and deep learning based image and feature analysis and classification) have their
background in biology.
Some strands of computer vision research are closely related to the study of biological vision – indeed, just as many
strands of AI research are closely tied with research into human consciousness, and the use of stored knowledge to
interpret, integrate and utilize visual information. The field of biological vision studies and models the physiological
processes behind visual perception in humans and other animals. Computer vision, on the other hand, studies and
describes the processes implemented in software and hardware behind artificial vision systems. Interdisciplinary
exchange between biological and computer vision has proven fruitful for both fields.
Yet another field related to computer vision is signal processing. Many methods for processing of one-variable
signals, typically temporal signals, can be extended in a natural way to processing of two-variable signals or
multi-variable signals in computer vision. However, because of the specific nature of images there are many methods
developed within computer vision which have no counterpart in processing of one-variable signals. Together with
the multi-dimensionality of the signal, this defines a subfield in signal processing as a part of computer vision.
44
Computer vision
Beside the above mentioned views on computer vision, many of the related research topics can also be studied from
a purely mathematical point of view. For example, many methods in computer vision are based on statistics,
optimization or geometry. Finally, a significant part of the field is devoted to the implementation aspect of computer
vision; how existing methods can be realized in various combinations of software and hardware, or how these
methods can be modified in order to gain processing speed without losing too much performance.
The fields most closely related to computer vision are image processing, image analysis and machine vision. There is
a significant overlap in the range of techniques and applications that these cover. This implies that the basic
techniques that are used and developed in these fields are more or less identical, something which can be interpreted
as there is only one field with different names. On the other hand, it appears to be necessary for research groups,
scientific journals, conferences and companies to present or market themselves as belonging specifically to one of
these fields and, hence, various characterizations which distinguish each of the fields from the others have been
presented.
Computer vision is, in some ways, the inverse of computer graphics. While computer graphics produces image data
from 3D models, computer vision often produces 3D models from image data. There is also a trend towards a
combination of the two disciplines, e.g., as explored in augmented reality.
The following characterizations appear relevant but should not be taken as universally accepted:
• Image processing and image analysis tend to focus on 2D images, how to transform one image to another, e.g., by
pixel-wise operations such as contrast enhancement, local operations such as edge extraction or noise removal, or
geometrical transformations such as rotating the image. This characterization implies that image
processing/analysis neither require assumptions nor produce interpretations about the image content.
• Computer vision includes 3D analysis from 2D images. This analyzes the 3D scene projected onto one or several
images, e.g., how to reconstruct structure or other information about the 3D scene from one or several images.
Computer vision often relies on more or less complex assumptions about the scene depicted in an image.
• Machine vision is the process of applying a range of technologies & methods to provide imaging-based automatic
inspection, process control and robot guidance in industrial applications. Machine vision tends to focus on
applications, mainly in manufacturing, e.g., vision based autonomous robots and systems for vision based
inspection or measurement. This implies that image sensor technologies and control theory often are integrated
with the processing of image data to control a robot and that real-time processing is emphasised by means of
efficient implementations in hardware and software. It also implies that the external conditions such as lighting
can be and are often more controlled in machine vision than they are in general computer vision, which can
enable the use of different algorithms.
• There is also a field called imaging which primarily focus on the process of producing images, but sometimes also
deals with processing and analysis of images. For example, medical imaging includes substantial work on the
analysis of image data in medical applications.
• Finally, pattern recognition is a field which uses various methods to extract information from signals in general,
mainly based on statistical approaches and artificial neural networks. A significant part of this field is devoted to
applying these methods to image data.
45
Computer vision
Applications for computer vision
One of the most prominent application fields is medical computer
vision or medical image processing. This area is characterized by the
extraction of information from image data for the purpose of making a
medical diagnosis of a patient. Generally, image data is in the form of
microscopy images, X-ray images, angiography images, ultrasonic
images, and tomography images. An example of information which
can be extracted from such image data is detection of tumours,
DARPA's Visual Media Reasoning concept video
arteriosclerosis or other malign changes. It can also be measurements
of organ dimensions, blood flow, etc. This application area also
supports medical research by providing new information, e.g., about the structure of the brain, or about the quality of
medical treatments. Applications of computer vision in the medical area also includes enhancement of images that
are interpreted by humans, for example ultrasonic images or X-ray images, to reduce the influence of noise.
A second application area in computer vision is in industry, sometimes called machine vision, where information is
extracted for the purpose of supporting a manufacturing process. One example is quality control where details or
final products are being automatically inspected in order to find defects. Another example is measurement of
position and orientation of details to be picked up by a robot arm. Machine vision is also heavily used in agricultural
process to remove undesirable food stuff from bulk material, a process called optical sorting.
Military applications are probably one of the largest areas for computer vision. The obvious examples are detection
of enemy soldiers or vehicles and missile guidance. More advanced systems for missile guidance send the missile to
an area rather than a specific target, and target selection is made when the missile reaches the area based on locally
acquired image data. Modern military concepts, such as "battlefield awareness", imply that various sensors,
including image sensors, provide a rich set of information about a combat scene which can be used to support
strategic decisions. In this case, automatic processing of the data is used to reduce complexity and to fuse
information from multiple sensors to increase reliability.
One of the newer application areas is autonomous vehicles, which
include submersibles, land-based vehicles (small robots with
wheels, cars or trucks), aerial vehicles, and unmanned aerial
vehicles (UAV). The level of autonomy ranges from fully
autonomous (unmanned) vehicles to vehicles where computer
vision based systems support a driver or a pilot in various
situations. Fully autonomous vehicles typically use computer
vision for navigation, i.e. for knowing where it is, or for producing
a map of its environment (SLAM) and for detecting obstacles. It
can also be used for detecting certain task specific events, e.g., a
UAV looking for forest fires. Examples of supporting systems are
Artist's Concept of Rover on Mars, an example of an
unmanned land-based vehicle. Notice the stereo
obstacle warning systems in cars, and systems for autonomous
cameras mounted on top of the Rover.
landing of aircraft. Several car manufacturers have demonstrated
systems for autonomous driving of cars, but this technology has
still not reached a level where it can be put on the market. There are ample examples of military autonomous
vehicles ranging from advanced missiles, to UAVs for recon missions or missile guidance. Space exploration is
already being made with autonomous vehicles using computer vision, e.g., NASA's Mars Exploration Rover and
ESA's ExoMars Rover.
Other application areas include:
• Support of visual effects creation for cinema and broadcast, e.g., camera tracking (matchmoving).
46
Computer vision
• Surveillance.
Typical tasks of computer vision
Each of the application areas described above employ a range of computer vision tasks; more or less well-defined
measurement problems or processing problems, which can be solved using a variety of methods. Some examples of
typical computer vision tasks are presented below.
Recognition
The classical problem in computer vision, image processing, and machine vision is that of determining whether or
not the image data contains some specific object, feature, or activity. This task can normally be solved robustly and
without effort by a human, but is still not satisfactorily solved in computer vision for the general case – arbitrary
objects in arbitrary situations. The existing methods for dealing with this problem can at best solve it only for
specific objects, such as simple geometric objects (e.g., polyhedra), human faces, printed or hand-written characters,
or vehicles, and in specific situations, typically described in terms of well-defined illumination, background, and
pose of the object relative to the camera.
Different varieties of the recognition problem are described in the literature:
• Object recognition – one or several pre-specified or learned objects or object classes can be recognized, usually
together with their 2D positions in the image or 3D poses in the scene. Google Goggles provides a stand-alone
program illustration of this function.
• Identification – an individual instance of an object is recognized. Examples include identification of a specific
person's face or fingerprint, identification of handwritten digits, or identification of a specific vehicle.
• Detection – the image data are scanned for a specific condition. Examples include detection of possible abnormal
cells or tissues in medical images or detection of a vehicle in an automatic road toll system. Detection based on
relatively simple and fast computations is sometimes used for finding smaller regions of interesting image data
which can be further analyzed by more computationally demanding techniques to produce a correct interpretation.
Several specialized tasks based on recognition exist, such as:
• Content-based image retrieval – finding all images in a larger set of images which have a specific content. The
content can be specified in different ways, for example in terms of similarity relative a target image (give me all
images similar to image X), or in terms of high-level search criteria given as text input (give me all images which
contains many houses, are taken during winter, and have no cars in them).
• Pose estimation – estimating the position or orientation of a specific object relative to the camera. An example
application for this technique would be assisting a robot arm in retrieving objects from a conveyor belt in an
assembly line situation or picking parts from a bin.
• Optical character recognition (OCR) – identifying characters in images of printed or handwritten text, usually
with a view to encoding the text in a format more amenable to editing or indexing (e.g. ASCII).
• 2D Code reading Reading of 2D codes such as data matrix and QR codes.
• Facial recognition
47
Computer vision
Motion analysis
Several tasks relate to motion estimation where an image sequence is processed to produce an estimate of the
velocity either at each points in the image or in the 3D scene, or even of the camera that produces the images .
Examples of such tasks are:
• Egomotion – determining the 3D rigid motion (rotation and translation) of the camera from an image sequence
produced by the camera.
• Tracking – following the movements of a (usually) smaller set of interest points or objects (e.g., vehicles or
humans) in the image sequence.
• Optical flow – to determine, for each point in the image, how that point is moving relative to the image plane,
i.e., its apparent motion. This motion is a result both of how the corresponding 3D point is moving in the scene
and how the camera is moving relative to the scene.
Scene reconstruction
Given one or (typically) more images of a scene, or a video, scene reconstruction aims at computing a 3D model of
the scene. In the simplest case the model can be a set of 3D points. More sophisticated methods produce a complete
3D surface model. The advent of 3D imaging not requiring motion or scanning, and related processing algorithms is
enabling rapid advances in this field. Grid-based 3D sensing can be used to acquire 3D images from multiple angles.
Algorithms are now available to stitch multiple 3D images together into point clouds and 3D models.
Image restoration
The aim of image restoration is the removal of noise (sensor noise, motion blur, etc.) from images. The simplest
possible approach for noise removal is various types of filters such as low-pass filters or median filters. More
sophisticated methods assume a model of how the local image structures look like, a model which distinguishes them
from the noise. By first analysing the image data in terms of the local image structures, such as lines or edges, and
then controlling the filtering based on local information from the analysis step, a better level of noise removal is
usually obtained compared to the simpler approaches.
An example in this field is the inpainting.
Computer vision system methods
The organization of a computer vision system is highly application dependent. Some systems are stand-alone
applications which solve a specific measurement or detection problem, while others constitute a sub-system of a
larger design which, for example, also contains sub-systems for control of mechanical actuators, planning,
information databases, man-machine interfaces, etc. The specific implementation of a computer vision system also
depends on if its functionality is pre-specified or if some part of it can be learned or modified during operation.
Many functions are unique to the application. There are, however, typical functions which are found in many
computer vision systems.
• Image acquisition – A digital image is produced by one or several image sensors, which, besides various types
of light-sensitive cameras, include range sensors, tomography devices, radar, ultra-sonic cameras, etc. Depending
on the type of sensor, the resulting image data is an ordinary 2D image, a 3D volume, or an image sequence. The
pixel values typically correspond to light intensity in one or several spectral bands (gray images or colour
images), but can also be related to various physical measures, such as depth, absorption or reflectance of sonic or
electromagnetic waves, or nuclear magnetic resonance.
• Pre-processing – Before a computer vision method can be applied to image data in order to extract some specific
piece of information, it is usually necessary to process the data in order to assure that it satisfies certain
assumptions implied by the method. Examples are
48
Computer vision
• Re-sampling in order to assure that the image coordinate system is correct.
• Noise reduction in order to assure that sensor noise does not introduce false information.
• Contrast enhancement to assure that relevant information can be detected.
• Scale space representation to enhance image structures at locally appropriate scales.
• Feature extraction – Image features at various levels of complexity are extracted from the image data. Typical
examples of such features are
• Lines, edges and ridges.
• Localized interest points such as corners, blobs or points.
More complex features may be related to texture, shape or motion.
• Detection/segmentation – At some point in the processing a decision is made about which image points or
regions of the image are relevant for further processing. Examples are
• Selection of a specific set of interest points
• Segmentation of one or multiple image regions which contain a specific object of interest.
• High-level processing – At this step the input is typically a small set of data, for example a set of points or an
image region which is assumed to contain a specific object. The remaining processing deals with, for example:
• Verification that the data satisfy model-based and application specific assumptions.
• Estimation of application specific parameters, such as object pose or object size.
• Image recognition – classifying a detected object into different categories.
• Image registration – comparing and combining two different views of the same object.
• Decision making Making the final decision required for the application, for example:
• Pass/fail on automatic inspection applications
• Match / no-match in recognition applications
• Flag for further human review in medical, military, security and recognition applications
Computer vision hardware
There are many kind of computer vision systems, nevertheless all of them contains these basic elements: power
source, at least one image acquisition device (i.e. camera, ccd, etc), processor as well as control and communication
cables or some kind of wireless interconnexion mechanism. In addition a practical vision system contains software
for application and develop as well as a display in order to monitor what does the system do. Vision system for inner
spaces, as most industrial ones, contains in addition an illumination system and in most cases a controlled
environment, specially on external lighting. Furthermore, a completed system includes many accessories like camera
supports, cables and connectors.
References
Further reading
•
•
•
•
David Marr (1982). Vision. W. H. Freeman and Company. ISBN 0-7167-1284-9.
Azriel Rosenfeld and Avinash Kak (1982). Digital Picture Processing. Academic Press. ISBN 0-12-597301-2.
Berthold Klaus Paul Horn (1986). Robot Vision. MIT Press. ISBN 0-262-08159-8.
Olivier Faugeras (1993). Three-Dimensional Computer Vision, A Geometric Viewpoint. MIT Press.
ISBN 0-262-06158-9.
• Tony Lindeberg (1994). Scale-Space Theory in Computer Vision (http://www.nada.kth.se/~tony/book.html).
Springer. ISBN 0-7923-9418-6.
• James L. Crowley and Henrik I. Christensen (Eds.) (1995). Vision as Process. Springer-Verlag.
ISBN 3-540-58143-X.
49
Computer vision
• Gösta H. Granlund and Hans Knutsson (1995). Signal Processing for Computer Vision. Kluwer Academic
Publisher. ISBN 0-7923-9530-1.
• Reinhard Klette, Karsten Schluens and Andreas Koschan (1998). Computer Vision – Three-Dimensional Data
from Images (http://www.cs.auckland.ac.nz/~rklette/Books/SpringerCV98/Springer98.html). Springer,
Singapore. ISBN 981-3083-71-9.
• Emanuele Trucco and Alessandro Verri (1998). Introductory Techniques for 3-D Computer Vision. Prentice Hall.
ISBN 0-13-261108-2.
• Bernd Jähne (2002). Digital Image Processing. Springer. ISBN 3-540-67754-2.
• Richard Hartley and Andrew Zisserman (2003). Multiple View Geometry in Computer Vision. Cambridge
University Press. ISBN 0-521-54051-8.
• Gérard Medioni and Sing Bing Kang (2004). Emerging Topics in Computer Vision. Prentice Hall.
ISBN 0-13-101366-1.
• R. Fisher, K Dawson-Howe, A. Fitzgibbon, C. Robertson, E. Trucco (2005). Dictionary of Computer Vision and
Image Processing. John Wiley. ISBN 0-470-01526-8.
• Nikos Paragios and Yunmei Chen and Olivier Faugeras (2005). Handbook of Mathematical Models in Computer
Vision (http://www.mas.ecp.fr/vision/Personnel/nikos/paragios-chen-faugeras/). Springer.
ISBN 0-387-26371-3.
• Wilhelm Burger and Mark J. Burge (2007). Digital Image Processing: An Algorithmic Approach Using Java
(http://www.imagingbook.com/). Springer. ISBN 1-84628-379-5.
• Pedram Azad, Tilo Gockel, Rüdiger Dillmann (2008). Computer Vision – Principles and Practice (http://ivt.
sourceforge.net/book.html). Elektor International Media BV. ISBN 0-905705-71-8.
• Richard Szeliski (2010). Computer Vision: Algorithms and Applications (http://szeliski.org/Book/).
Springer-Verlag. ISBN 978-1848829343.
External links
• USC Iris computer vision conference list (http://iris.usc.edu/Information/Iris-Conferences.html)
• Computer vision papers on the web (http://www.cvpapers.com/index.html) A complete list of papers of the
most relevant computer vision conferences.
• Computer Vision Online (http://www.computervisiononline.com/) News, source code, datasets and job offers
related to computer vision.
• Keith Price's Annotated Computer Vision Bibliography (http://iris.usc.edu/Vision-Notes/bibliography/
contents.html)
• CVonline (http://homepages.inf.ed.ac.uk/rbf/CVonline/) Bob Fisher's Compendium of Computer Vision.
50
Mobile manipulator
51
Mobile manipulator
Definition
Mobile manipulator is nowadays a widespread term to refer to
robot systems built from a robotic manipulator arm mounted on a
mobile platform. Such systems combine the advantages of mobile
platforms and robotic manipulator arms and reduce their
drawbacks. For instance, the mobile platform extends the
workspace of the arm, whereas an arm offers several operational
functionalities.
A mobile manipulation system offers a dual advantage of
mobility offered by a mobile platform and dexterity offered by
the manipulator. The mobile platform offers unlimited workspace
to the manipulator. The extra degrees of freedom of the mobile
platform also provide user with more choices. However the
operation of such a system is challenging because of the many
degrees of freedom and the unstructured environment that it
performs in.
Mobile Manipulator systems; mobile platform, robot
manipulator, vision and tooling
General system composition:
•
•
•
•
Mobile platform
Robot manipulator
Vision
Tooling
Motivation
At the moment mobile manipulation is a subject of major focus in development and research environments, and
mobile manipulators, either autonomous or teleoperated, are used in many different areas, e.g. space exploration,
military operations, home-care and health-care. However, within the industrial field the implementation of mobile
manipulators has been limited, although the needs for intelligent and flexible automation are present. In addition, the
necessary technology entities (mobile platforms, robot manipulators, vision and tooling) are, to a large extent,
available off-the-shelf components.[1]
A reason for this is that the manufacturing industries act traditionally and, therefore, have reluctance in taking risks
by implementing new technologies. Also within the field of industrial mobile manipulation the centre of attention
has been on optimization of the individual technologies, especially robot manipulators [2] and tooling,[3] while the
integration, use and application have been neglected. This means that few implementations of mobile robots, in
production environments, have been reported - e.g.[4] and.[5]
Timeline
Mobile manipulator
52
Year
Robot name
Company / Research Institute
1996 Hilare 2bis [6]
LAAS-CNRS, France
2000 Jaume
Robotic Intelligence Lab, Jaume I University, Spain
2004 FAuStO
University of Verona, Italy
2006 Neobotix MM-500 [7] Neobotix GmbH, Germany
2009 Little Helper [8]
Department of Production, Aalborg University, Denmark
2012 GWAM [9]
Robotnik Automation & Barrett Technologies, Spain
2013 UBR-1 [10]
Unbounded Robotics, USA
& USA
State of the art
One recent example is the mobile manipulator "Little Helper"
from the Department of Production at Aalborg University.[11]
Notes and references
[1] M. Hvilshøj, S. Bøgh, O. Madsen and M. Kristiansen: The Mobile Robot “Little
Helper”: Concepts, ideas and working principles, 14th IEEE International
Conference on Emerging Techonologies and Factory Automation, 2009
[2] A. Albu-Schäffer, S. Haddadin, C. Ott, A. Stemmer, T. Wimböck and G.
Hirzinger: The DLR lightweight robot: design and control concepts for robots
in human environments, Industrial Robot, vol. 34, no. 5, pp. 376-385, 2007
[3] H. Liu, P. Meusel, G. Hirzinger, M. Jin and Y. X. Liu: The Modular
Multisensory DLR-HIT-Hand: Hardware and Software Architecture,
IEEE/ASME Transactions on Mechatronics, vol. 13, no. 4, pp. 461-469, 2008
[4] A. Stopp, S. Horstmann, S. Kristensen and F. Lohnert: Towards Interactive
Learning for Manufacturing Assistant, IEEE Transactions on Industrial
Electronics, pp. 705-707, 2003
[5] E. Helms, R. D. Schraft and M. Hägele: rob@work: Robot assistant in
industrial environments, Proceedings in IEEE International Workshop on Robot
and Human Interactive Communication, pp. 399-404, 2002
[6] http:/ / homepages. laas. fr/ matthieu/ robots/ h2bis. shtml
[7] http:/ / www. neobotix-roboter. de/ automation-robotics-mm-500-sk. html
[8] http:/ / www. machinevision. dk
[9] http:/ / www. robotnik. es/ en/ products/ mobile-manipulators/ guardian-wam
[10] http:/ / unboundedrobotics. com/
[11] Research project; Industrial maturation and exploitation of mobile
manipulators - more info: MachineVision.dk (http:/ / www. machinevision. dk)
Mobile Manipulator: Little Helper - Aalborg
University
External links
• Automation Group, Department of Production (http://www.machinevision.dk), Aalborg University
Robot locomotion
Robot locomotion
Robot locomotion is the collective name for the various methods that robots use to transport themselves from place
to place. Although wheeled robots are typically quite energy efficient and simple to control, other forms of
locomotion may be more appropriate for a number of reasons (e.g. traversing rough terrain, moving and interacting
in human environments). Furthermore, studying bipedal and insect-like robots may beneficially impact on
biomechanics.
A major goal in this field is in developing capabilities for robots to autonomously decide how, when, and where to
move. However, coordinating a large number of robot joints for even simple matters, like negotiating stairs, is
difficult. Autonomous robot locomotion is a major technological obstacle for many areas of robotics, such as
humanoids (like Honda's Asimo).
Types of locomotion
Wheeled
In terms of energy efficiency on flat surfaces, wheeled robots are the most efficient. This is due to the fact that an
ideal rolling (but not slipping) wheel loses no energy. A wheel rolling at a given velocity needs no input to maintain
its motion. This is in contrast to legged robots which suffer an impact with the ground at heelstrike and lose energy
as a result.
There are many different types of wheeled robots, the most common being the Reed Shepps type and the unicycle
type. The major concern in the motion planning of wheeled robots are the holonomic that the robot is subject to.
These are decided by the type of wheels, number of wheels and the direction of the axes of rotation of the wheels.
Examples
• iRobot's Roomba
• Various DARPA Grand Challenge entries
Walking
• See Hexapod (robotics)
Bipedal walking
• Passive dynamics
• Zero Moment Point
Running
•
•
•
•
•
ASIMO
BigDog
HUBO 2
RunBot
Toyota Partner Robot
53
Robot locomotion
54
Rolling
For simplicity most mobile robots have four wheels or a number of
continuous tracks. Some researchers have tried to create more complex
wheeled robots with only one or two wheels. These can have certain
advantages such as greater efficiency and reduced parts, as well as
allowing a robot to navigate in confined places that a four wheeled
robot would not be able to.
Examples
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Boe-Bot
Cosmobot
Elmer
Elsie
Enon
HERO
IRobot Create
Johns Hopkins Beast
Land Walker
Modulus robot
Musa
Omnibot
PaPeRo
Phobot
Pocketdelta robot
Push the Talking Trash Can
RB5X
Rovio
Seropi
Shakey the robot
Sony Rolly
Spykee
TiLR
Topo
Segway in the Robot museum in Nagoya.
• TR Araña
• Wakamaru
Hopping
Several robots, built in the 1980s by Marc Raibert at the MIT Leg Laboratory, successfully demonstrated very
dynamic walking. Initially, a robot with only one leg, and a very small foot, could stay upright simply by hopping.
The movement is the same as that of a person on a pogo stick. As the robot falls to one side, it would jump slightly in
that direction, in order to catch itself. Soon, the algorithm was generalised to two and four legs. A bipedal robot was
demonstrated running and even performing somersaults. A quadruped was also demonstrated which could trot, run,
pace, and bound. For a full list of these robots, see the MIT Leg Lab Robots [20] page.
Robot locomotion
Metachronal motion
Coordinated, sequential mechanical action having the appearance of a traveling wave is called a metachronal rhythm
or wave, and is employed in nature by ciliates for transport, and by worms and arthropods for locomotion.
Slithering
Several snake robots have been successfully developed. Mimicking the way real snakes move, these robots can
navigate very confined spaces, meaning they may one day be used to search for people trapped in collapsed
buildings. The Japanese ACM-R5 snake robot[1] can even navigate both on land and in water.[2]
Examples
• Snake-arm robot
• Roboboa
• Snakebot
Swimming
• Autonomous underwater vehicles
Brachiating
Brachiation allows robots to travel by swinging, only using energy to grab and release surfaces.[3]
Approaches
•
•
•
•
•
Gait engineering
Product optimization
Motion planning
Motion capture may be performed on humans, insects and other organisms.
Machine learning, typically with reinforcement learning.
Notable researchers in the field
•
•
•
•
Rodney Brooks
Marc Raibert
Jessica Hodgins
Red Whittaker
References
[1] ACM-R5 (http:/ / www-robot. mes. titech. ac. jp/ robot/ snake/ acm-r5/ acm-r5_e. html)
[2] Swimming snake robot (commentary in Japanese) (http:/ / video. google. com/ videoplay?docid=139523333240485714)
[3] "Video: Brachiating 'Bot Swings Its Arm Like An Ape" (http:/ / www. popsci. com/ technology/ article/ 2012-05/
video-gibbot-brachiating-bot-swings-its-arm-ape)
External links
• Robot Locomotion (http://www.robotplatform.com/knowledge/Classification_of_Robots/
Holonomic_and_Non-Holonomic_drive.html)
55
Mobile robot
56
Mobile robot
A mobile robot is an automatic machine that is capable of movement in any given environment.
Mobile robots have the capability to move around in their environment
and are not fixed to one physical location. In contrast, industrial robots
usually consist of a jointed arm (multi-linked manipulator) and gripper
assembly (or end effector) that is attached to a fixed surface.
Mobile robots are a major focus of current research and almost every
major university has one or more labs that focus on mobile robot
research. Mobile robots are also found in industry, military and
security environments. Domestic robots are consumer products,
including entertainment robots and those that perform certain
household tasks such as vacuuming or gardening.
A spying robot is an example of a mobile robot
[1]
capable of movement in a given environment.
Classification
Mobile robots may be classified by:
• The environment in which they travel:
• Land or home robots are usually referred to as Unmanned Ground Vehicles (UGVs). They are most commonly
wheeled or tracked, but also include legged robots with two or more legs (humanoid, or resembling animals or
insects).
• Aerial robots are usually referred to as Unmanned Aerial Vehicles (UAVs)
• Underwater robots are usually called autonomous underwater vehicles (AUVs)
• Polar robots, designed to navigate icy, crevasse filled environments
• The device they use to move, mainly:
• Legged robot : human-like legs (i.e. an android) or animal-like legs.
• Wheeled robot.
• Tracks.[2]
Mobile robot navigation
There are many types of mobile robot navigation:
Manual remote or tele-op
A manually teleoperated robot is totally under control of a driver with a joystick or other control device. The device
may be plugged directly into the robot, may be a wireless joystick, or may be an accessory to a wireless computer or
other controller. A tele-op'd robot is typically used to keep the operator out of harm's way. Examples of manual
remote robots include Robotics Design's ANATROLLER ARI-100 and ARI-50, Foster-Miller's Talon, iRobot's
PackBot, and KumoTek's MK-705 Roosterbot.
Mobile robot
Guarded tele-op
A guarded tele-op robot has the ability to sense and avoid obstacles but will otherwise navigate as driven, like a
robot under manual tele-op. Few if any mobile robots offer only guarded tele-op. (See Sliding Autonomy below.)
Line-following Car
Some of the earliest Automated Guided Vehicles (AGVs) were line following mobile robots. They might follow a
visual line painted or embedded in the floor or ceiling or an electrical wire in the floor. Most of these robots operated
a simple "keep the line in the center sensor" algorithm. They could not circumnavigate obstacles; they just stopped
and waited when something blocked their path. Many examples of such vehicles are still sold, by Transbotics, FMC,
Egemin, HK Systems and many other companies.
Autonomously randomized robot
Autonomous robots with random motion basically bounce off walls, whether those walls are sensed
Autonomously guided robot
An autonomously guided robot knows at least some information about
where it is and how to reach various goals and or waypoints along the
way. "Localization" or knowledge of its current location, is calculated
by one or more means, using sensors such motor encoders, vision,
Stereopsis, lasers and global positioning systems. Positioning systems
often use triangulation, relative position and/or Monte-Carlo/Markov
localization to determine the location and orientation of the platform,
from which it can plan a path to its next waypoint or goal. It can gather
sensor readings that are time- and location-stamped, so that a hospital,
for instance, can know exactly when and where radiation levels
exceeded permissible levels. Such robots are often part of the wireless
enterprise network, interfaced with other sensing and control systems
Robot developers use ready-made autonomous
bases and software to design robot applications
in the building. For instance, the PatrolBot security robot responds to
quickly. Shells shaped like people or cartoon
alarms, operates elevators and notifies the command center when an
characters may cover the base to disguise it.
incident arises. Other autonomously guided robots include the
Courtesy of MobileRobots Inc
SpeciMinder and the Tug delivery robots for hospital labs, though the
latter actually has people at the ready to drive the robot remotely when its autonomy fails. The Tug sends a letter to
its tech support person, who then takes the helm and steers it over the Internet by looking through a camera low in
the base of the robot.
Sliding autonomy
More capable robots combine multiple levels of navigation under a system called sliding autonomy. Most
autonomously guided robots, such as the HelpMate hospital robot, also offer a manual mode. The Motivity
autonomous robot operating system, which is used in the ADAM, PatrolBot, SpeciMinder, MapperBot and a number
of other robots, offers full sliding autonomy, from manual to guarded to autonomous modes.
Also see Autonomous robot
57
Mobile robot
58
History
Date
Developments
1939–1945 During World War II the first mobile robots emerged as a result of technical advances on a number of relatively new research fields
like computer science and cybernetics. They were mostly flying bombs. Examples are smart bombs that only detonate within a certain
range of the target, the use of guiding systems and radar control. The V1 and V2 rockets had a crude 'autopilot' and automatic
detonation systems. They were the predecessors of modern cruise missiles.
1948–1949 W. Grey Walter builds Elmer and Elsie, two autonomous robots called Machina Speculatrix because these robots liked to explore their
environment. Elmer and Elsie were each equipped with a light sensor. If they found a light source they would move towards it,
avoiding or moving obstacles on their way. These robots demonstrated that complex behaviour could arise from a simple design.
Elmer and Elsie only had the equivalent of two nerve cells.
1961–1963 The Johns Hopkins University develops 'Beast'. Beast used a sonar to move around. When its batteries ran low it would find a power
socket and plug itself in.
[3]
1969
Mowbot was the very first robot that would automatically mow the lawn.
1970
The Stanford Cart line follower was a mobile robot that was able to follow a white line, using a camera to see. It was radio linked to a
[4]
large mainframe that made the calculations.
At about the same time (1966–1972) the Stanford Research Institute is building and doing research on Shakey the Robot, a robot
named after its jerky motion. Shakey had a camera, a rangefinder, bump sensors and a radio link. Shakey was the first robot that could
reason about its actions. This means that Shakey could be given very general commands, and that the robot would figure out the
necessary steps to accomplish the given task.
The Soviet Union explores the surface of the Moon with Lunokhod 1, a lunar rover.
1976
In its Viking program the NASA sends two unmanned spacecraft to Mars.
1980
The interest of the public in robots rises, resulting in robots that could be purchased for home use. These robots served entertainment
or educational purposes. Examples include the RB5X, which still exists today and the HERO series.
The Stanford Cart is now able to navigate its way through obstacle courses and make maps of its environment.
Early
1980s
The team of Ernst Dickmanns at Bundeswehr University Munich builds the first robot cars, driving up to 55 mph on empty streets.
1987
Hughes Research Laboratories demonstrates the first cross-country map and sensor-based autonomous operation of a robotic
[5]
vehicle.
1989
Mark Tilden invents BEAM robotics.
1990s
Joseph Engelberger, father of the industrial robotic arm, works with colleagues to design the first commercially available autonomous
mobile hospital robots, sold by Helpmate. The US Department of Defense funds the MDARS-I project, based on the Cybermotion
indoor security robot.
1991
Edo. Franzi, André Guignard and Francesco Mondada developed Khepera, an autonomous small mobile robot intended for research
activities. The project was supported by the LAMI-EPFL lab.
1993–1994 Dante I and Dante II were developed by Carnegie Mellon University. Both were walking robots used to explore live volcanoes.
1994
With guests on board, the twin robot vehicles VaMP and VITA-2 of Daimler-Benz and Ernst Dickmanns of UniBwM drive more than
one thousand kilometers on a Paris three-lane highway in standard heavy traffic at speeds up to 130 km/h. They demonstrate
autonomous driving in free lanes, convoy driving, and lane changes left and right with autonomous passing of other cars.
1995
Semi-autonomous ALVINN steered a car coast-to-coast under computer control for all but about 50 of the 2850 miles. Throttle and
brakes, however, were controlled by a human driver.
1995
In the same year, one of Ernst Dickmanns' robot cars (with robot-controlled throttle and brakes) drove more than 1000 miles from
Munich to Copenhagen and back, in traffic, at up to 120 mph, occasionally executing maneuvers to pass other cars (only in a few
critical situations a safety driver took over). Active vision was used to deal with rapidly changing street scenes.
1995
The Pioneer programmable mobile robot becomes commercially available at an affordable price, enabling a widespread increase in
robotics research and university study over the next decade as mobile robotics becomes a standard part of the university curriculum.
Mobile robot
59
1996–1997 NASA sends the Mars Pathfinder with its rover Sojourner to Mars. The rover explores the surface, commanded from earth. Sojourner
was equipped with a hazard avoidance system. This enabled Sojourner to autonomously find it s way through unknown martian
terrain.
1999
Sony introduces Aibo, a robotic dog capable of seeing, walking and interacting with its environment. The PackBot remote-controlled
military mobile robot is introduced.
2001
Start of the Swarm-bots project. Swarm bots resemble insect colonies. Typically they consist of a large number of individual simple
robots, that can interact with each other and together perform complex tasks. [6]
2002
Appears Roomba, a domestic autonomous mobile robot that cleans the floor.
2003
[7]
Axxon Robotics purchases Intellibot , manufacturer of a line of commercial robots that scrub, vacuum, and sweep floors in
hospitals, office buildings and other commercial buildings. Floor care robots from Intellibot Robotics LLC operate completely
autonomously, mapping their environment and using an array of sensors for navigation an obstacle avoidance.
2004
Robosapien, a biomorphic toy robot designed by Mark Tilden is commercially available.
In 'The Centibots Project' 100 autonomous robots work together to make a map of an unknown environment and search for objects
within the environment.
In the first DARPA Grand Challenge competition, fully autonomous vehicles compete against each other on a desert course.
2005
Boston Dynamics creates a quadruped robot intended to carry heavy loads across terrain too rough for vehicles.
2006
Sony stops making Aibo and HelpMate halts production, but a lower-cost PatrolBot customizable autonomous service robot system
becomes available as mobile robots continue the struggle to become commercially viable. The US Department of Defense drops the
MDARS-I project, but funds MDARS-E, an autonomous field robot. TALON-Sword, the first commercially available robot with
grenade launcher and other integrated weapons options, is released. Honda's Asimo learns to run and climb stairs.
2007
In the DARPA Urban Grand Challenge, six vehicles autonomously comple a complex course involving manned vehicles and
[8]
obstacles. Kiva Systems clever robots proliferate in distribution operations; these smart shelving units sort themselves according to
the popularity of their contents. The Tug becomes a popular means for hospitals to move large cabinets of stock from place to place,
while the Speci-Minder [9] with Motivity begins carrying blood and other patient samples from nurses' stations to various labs.
Seekur, the first widely available, non-military outdoor service robot, pulls a 3-ton vehicle across a parking lot, drives autonomously
indoors and begins learning how to navigate itself outside. Meanwhile, PatrolBot learns to follow people and detect doors that are ajar.
2008
Boston Dynamics released video footage of a new generation BigDog able to walk on icy terrain and recover its balance when kicked
from the side.
2010
The Multi Autonomous Ground-robotic International Challenge has teams of autonomous vehicles map a large dynamic urban
environment, identify and track humans and avoid hostile objects.
References
[1] Optically Automated Spy Robot, 'OASR', Gaurav Mittal and Deepansh Sehgal, Punjab Engineering College
[2] Rail track (http:/ / prweb. com/ releases/ Rail/ Robot/ prweb453019. htm) and Linear track (PDF) (http:/ / www. labautomationrobots. com/
images/ crstrack. pdf)
[3] http:/ / www. frc. ri. cmu. edu/ ~hpm/ talks/ Extras/ mowbot. 1969. gif
[4] Les Earnest (http:/ / www. stanford. edu/ ~learnest/ cart. htm)
[5] Proceedings of IEEE Robotics and Automation, 1988
[6] http:/ / www. swarm-bots. org/
[7] http:/ / www. intellibotrobotics. com/
[8] [http://www.darpa.mil/GRANDCHALLENGE/ Welcome
[9] http:/ / www. speciminder. com/
Mobile robot
60
External links
• A tutorial about line tracking sensors and algorithms (http://ikalogic.com/tut_line_sens_algo.php)
• BioRobotics Laboratory, Research in Mobile Robotics and Human-Robot Interaction (http://robot.kut.ac.kr)
• Department of Production at Aalborg University in Denmark, Research in Mobile Robotics and Manipulation
(http://www.machinevision.dk)
Robotic mapping
Robotic mapping is a discipline related to cartography. The
goal for an autonomous robot to be able to construct (or use) a
map or floor plan and to localize itself in it. Robotic mapping
is that branch of one, which deals with the study and
application of ability to construct map or floor plan by the
autonomous robot and to localize itself in it.
Evolutionarily shaped blind action may suffice to keep some
animals alive. For some insects for example, the environment
is not interpreted as a map, and they survive only with a
triggered response. A slightly more elaborated navigation
strategy dramatically enhances the capabilities of the robot.
Cognitive maps enable planning capacities, and use of current
perceptions, memorized events, and expected consequences.
Robotic mapping can be used for serving robot guide
Operation
The robot has two sources of information: the idiothetic and the allothetic sources. When in motion, a robot can use
dead reckoning methods such as tracking the number of revolutions of its wheels; this corresponds to the idiothetic
source and can give the absolute position of the robot, but it is subject to cumulative error which can grow quickly.
The allothetic source corresponds to the sensors of the robot, like a camera, a microphone, laser, lidar or sonar. The
problem here is "perceptual aliasing". This means that two different places can be perceived as the same. For
example, in a building, it is nearly impossible to determine a location solely with the visual information, because all
the corridors may look the same.
Map representation
The internal representation of the map can be "metric" or "topological":
• The metric framework is the most common for humans and considers a two-dimensional space in which it places
the objects. The objects are placed with precise coordinates. This representation is very useful, but is sensitive to
noise and it is difficult to calculate the distances precisely.
• The topological framework only considers places and relations between them. Often, the distances between places
are stored. The map is then a graph, in which the nodes corresponds to places and arcs correspond to the paths.
Many techniques use probabilistic representations of the map, in order to handle uncertainty.
There are three main methods of map representations, i.e., free space maps, object maps, and composite maps. These
employ the notion of a grid, but permit the resolution of the grid to vary so that it can become finer where more
accuracy is needed and more coarse where the map is uniform.
Robotic mapping
61
Map learning
Map-learning cannot be separated from the localization process, and a difficulty arises when errors in localization are
incorporated into the map. This problem is commonly referred to as Simultaneous localization and mapping
(SLAM).
An important additional problem is to determine whether the robot is in a part of environment already stored or never
visited. One way to solve this problem is by using electric beacons.
Path planning
Path planning is an important issue as it allows a robot to get from point A to point B. Path planning algorithms are
measured by their computational complexity. The feasibility of real-time motion planning is dependent on the
accuracy of the map (or floorplan), on robot localization and on the number of obstacles. Topologically, the problem
of path planning is related to the shortest path problem problem of finding a route between two nodes in a graph.
Robot navigation
Outdoor robots can use GPS in a similar way to automotive navigation systems. Alternative systems can be used
with floor plan instead of maps for indoor robots, combined with localization wireless hardware. Electric beacons
also have been proposed for cheap robot navigational systems.
References
Human–robot interaction
Human–robot interaction is the study of interactions between humans and robots. It is often referred as HRI by
researchers. Human–robot interaction is a multidisciplinary field with contributions from human–computer
interaction, artificial intelligence, robotics, natural language understanding, design, and social sciences.
Origins
Human–robot interaction has been a topic of both science fiction and academic speculation even before any robots
existed. Because HRI depends on a knowledge of (sometimes natural) human communication, many aspects of HRI
are continuations of human communications topics that are much older than robotics per se.
The origin of HRI as a discrete problem was stated by 20th-century author Isaac Asimov in 1941, in his novel I,
Robot. He states the Three Laws of Robotics as,
1. A robot may not injure a human being or, through inaction, allow a human being to come to harm.
2. A robot must obey any orders given to it by human beings, except where such orders would conflict with the First Law.
3. A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.
“
”
These three laws of robotics determine the idea of safe interaction. The closer the human and the robot get and the
more intricate the relationship becomes, the more the risk of a human being injured rises. Nowadays in advanced
societies, manufacturers employing robots solve this issue by not letting humans and robot share the workspace at
any time. This is achieved by the extensive use of safe zones and cages. Thus the presence of humans is completely
forbidden in the robot workspace while it is working.
With the advances of artificial intelligence, the autonomous robots could eventually have more proactive behaviors,
planning their motion in complex unknown environments. These new capabilities keep safety as the primary issue
Humanrobot interaction
62
and efficiency as secondary. To allow this new generation of robot, research is being made on human detection,
motion planning, scene reconstruction, intelligent behavior through task planning and compliant behavior using force
control (impedance or admittance control schemes).
The basic goal of HRI is to define a general human model that could lead to principles and algorithms allowing more
natural and effective interaction between humans and robots. Research ranges from how humans work with remote,
tele-operated unmanned vehicles to peer-to-peer collaboration with anthropomorphic robots.
Many in the field of HRI study how humans collaborate and interact and use those studies to motivate how robots
should interact with humans.
The goal of friendly human–robot interactions
Robots are artificial agents with capacities of perception and action in the
physical world often referred by researchers as workspace. Their use has been
generalized in factories but nowadays they tend to be found in the most
technologically advanced societies in such critical domains as search and
rescue, military battle, mine and bomb detection, scientific exploration, law
enforcement, entertainment and hospital care.
These new domains of applications imply a closer interaction with the user. The
concept of closeness is to be taken in its full meaning, robots and humans share
the workspace but also share goals in terms of task achievement. This close
interaction needs new theoretical models, on one hand for the robotics scientists
who work to improve the robots utility and on the other hand to evaluate the
risks and benefits of this new "friend" for our modern society.
Kismet can produce a range of facial
expressions.
With the advance in AI, the research is focusing on one part towards the safest
physical interaction but also on a socially correct interaction, dependent on
cultural criteria. The goal is to build an intuitive, and easy communication with the robot through speech, gestures,
and facial expressions.
Dautenhan refers to friendly Human–robot interaction as "Robotiquette" defining it as the "social rules for robot
behaviour (a ‘robotiquette’) that is comfortable and acceptable to humans" The robot has to adapt itself to our way of
expressing desires and orders and not the contrary. But every day environments such as homes have much more
complex social rules than those implied by factories or even military environments. Thus, the robot needs perceiving
and understanding capacities to build dynamic models of its surroundings. It needs to categorize objects, recognize
and locate humans and further their emotions. The need for dynamic capacities pushes forward every sub-field of
robotics.
On the other end of HRI research the cognitive modelling of the "relationship" between human and the robots
benefits the psychologists and robotic researchers the user study are often of interests on both sides. This research
endeavours part of human society.
Humanrobot interaction
General HRI research
HRI research spans a wide range of field, some general to the nature of HRI.
Methods for perceiving humans
Most methods intend to build a 3D model through vision of the environment. The proprioception sensors permit the
robot to have information over its own state. This information is relative to a reference.
Methods for perceiving humans in the environment are based on sensor information. Research on sensing
components and software lead by Microsoft provide useful results for extracting the human kinematics (see Kinect).
An example of older technique is to use colour information for example the fact that for light skinned people the
hands are lighter than the clothes worn. In any case a human modelled a priori can then be fitted to the sensor data.
The robot builds or has (depending on the level of autonomy the robot has) a 3D mapping of its surroundings to
which is assigned the humans locations.
A speech recognition system is used to interpret human desires or commands. By combining the information inferred
by proprioception, sensor and speech the human position and state (standing, seated).
Methods for motion planning
Motion planning in dynamic environment is a challenge that is for the moment only achieved for 3 to 10 degrees of
freedom robots. Humanoid robots or even 2 armed robots that can have up to 40 degrees of freedom are unsuited for
dynamic environments with today's technology. However lower dimensional robots can use potential field method to
compute trajectories avoiding collisions with human.
Cognitive models and theory of mind
A lot of data has been gathered with regards to user studies. For example, when users encounter proactive behaviour
on the part of the robot and the robot does not respect a safety distance, penetrating the user space, he or she might
express fear. This is dependent on one person to another. Only intensive experiment can permit a more precise
model.
It has been shown that when a robot has no particular use, negative feelings are often expressed. The robot is
perceived as useless and its presence becomes annoying.
In another experiment, it has occurred that people tend to attribute to the robot personality characteristics that were
not implemented.
Application-oriented HRI research
In addition to general HRI research, researchers are currently exploring application areas for human-robot interaction
systems. Application-oriented research is used to help bring current robotics technologies to bear against problems
that exist in today's society. While human-robot interaction is still a rather young area of interest, there is active
development and research in many areas.
Search and rescue
First responders face great risks in search and rescue (SAR) settings, which typically involve environments that are
unsafe for a human to travel[citation needed]. In addition, technology offers tools for observation that can greatly
speed-up and improve the accuracy of human perception[citation needed]. Robots can be used to address these
concerns[citation needed] . Research in this area includes efforts to address robot sensing, mobility, navigation,
planning, integration, and tele-operated control[citation needed].
SAR robots have already been deployed to environments such as the Collapse of the World Trade Center.
63
Humanrobot interaction
Other application areas include:
•
•
•
•
•
•
•
Entertainment
Education
Field robotics
Home and companion robotics
Hospitality
Rehabilitation and Elder Care
Robot Assisted Therapy (RAT)
Properties
Bartneck and Okada suggest that a robotic user interface can be described by the following four properties:
Tool – toy scale
• Is the system designed to solve a problem effectively or is it just for entertainment?
Remote control – autonomous scale
• Does the robot require remote control or is it capable of action without direct human influence?
Reactive – dialogue scale
• Does the robot rely on a fixed interaction pattern or is it able to have dialogue — exchange of information — with
a human?
Anthropomorphism scale
• Does it have the shape or properties of a human?
Conferences
International Conference on Social Robotics
The International Conference on Social Robotics is a conference for scientists, researchers, and practitioners to
report and discuss the latest progress of their forefront research and findings in social robotics, as well as interactions
with human beings and integration into our society.
• ICSR2009, Incheon, Korea in collaboration with the FIRA RoboWorld Congress
• ICSR2010 [1], Singapore
• ICSR2011 [2], Amsterdam, Netherlands
International Conference on Human-Robot Personal Relationships
• HRPR2008, Maastricht
• HRPR 2009 [3], Tilburg. Keynote speaker was Hiroshi Ishiguro.
• HRPR2010 [4], Leiden. Keynote speaker was Kerstin Dautenhahn.
International Symposium on New Frontiers in Human-Robot Interaction
This symposium is organized in collaboration with the Annual Convention of the Society for the Study of Artificial
Intelligence and Simulation of Behaviour.
• 2010 [5], Leicester, United Kingdom
• 2009 [6], Edinburgh, United Kingdom
64
Humanrobot interaction
IEEE International Symposium in Robot and Human Interactive Communication
The IEEE International Symposium on Robot and Human Interactive Communication [7] ( RO-MAN ) was founded
in 1992 by Profs. Toshio Fukuda, Hisato Kobayashi, Hiroshi Harashima and Fumio Hara. Early workshop
participants were mostly Japanese, and the first seven workshops were held in Japan. Since 1999, workshops have
been held in Europe and the United States as well as Japan, and participation has been of international scope.
ACM/IEEE International Conference on Human-Robot Interaction
This conference is amongst the best conferences in the field of HRI and has a very selective reviewing process. The
average acceptance rate is 26% and the average attendance is 187. Around 65% of the contributions to the
conference come from the USA and the high level of quality of the submissions to the conference becomes visible by
the average of 10 citations that the HRI papers attracted so far.
•
•
•
•
•
•
HRI 2006 [8] in Salt Lake City, Utah, USA, Acceptance Rate: 0.29
HRI 2007 [9] in Washington DC, USA, Acceptance Rate: 0.23
HRI 2008 [10] in Amsterdam, Netherlands, Acceptance Rate: 0.36 (0.18 for oral presentations)
HRI 2009 [11] in San Diego, CA, USA, Acceptance Rate: 0.19
HRI 2010 [12] in Osaka, Japan, Acceptance Rate: 0.21
HRI 2011 [13] in Lausanne, Switzerland, Acceptance Rate: 0.22
Related conferences
There are many conferences that are not exclusively HRI, but deal with broad aspects of HRI, and often have HRI
papers presented.
•
•
•
•
•
•
•
IEEE-RAS/RSJ International Conference on Humanoid Robots (Humanoids)
Ubiquitous Computing (UbiComp)
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Intelligent User Interfaces (IUI)
Computer Human Interaction (CHI)
American Association for Artificial Intelligence (AAAI)
INTERACT
Related journals
There are currently two dedicated HRI Journals
• International Journal of Social Robotics [14]
• The open access Journal of Human-Robot Interaction [15]
and a new dedicated HRI Journal
and there are several more general journals in which one will find HRI articles.
•
•
•
•
•
International Journal of Humanoid Robotics
Entertainment Robotics Section [16] of the Entertainment Computing Journal
Interaction Studies Journal [17]
Artificial Intelligence
Systems, Man and Cybernetics
65
Humanrobot interaction
Footnotes
[1] http:/ / www. icsr2010. org/
[2] http:/ / icsr2011. org/
[3] http:/ / hrpr. uvt. nl
[4] http:/ / hrpr. liacs. nl
[5] http:/ / homepages. feis. herts. ac. uk/ ~comqkd/ HRI-AISB2010-Symposium. html
[6] http:/ / homepages. feis. herts. ac. uk/ ~comqkd/ HRI-AISB2009-Symposium. html
[7] http:/ / www. ro-man. org/
[8] http:/ / www. hri2006. org/
[9] http:/ / www. hri2007. org/
[10] http:/ / www. hri2008. org/
[11] http:/ / www. hri2009. org/
[12] http:/ / www. hri2010. org/
[13] http:/ / www. hri2011. net/
[14] http:/ / www. springer. com/ engineering/ robotics/ journal/ 12369
[15] http:/ / humanrobotinteraction. org/ journal/
[16] http:/ / www. elsevier. com/ locate/ inca/ 717010
[17] http:/ / benjamins. com/ #catalog/ journals/ is
External links
• Human interaction with the robot J2B2 (http://www.hakenberg.de/automation/j2b2_human_interaction.htm):
Algorithms, graphics, and video material
• Carnegie Mellon University's People and Robots Research Group (http://www.peopleandrobots.org/):
Experimental and ethnographic studies of human-robot interaction
• Carnegie Mellon University's Human-Robot Interaction research group (http://www.ri.cmu.edu/
research_lab_group_detail.html?type=personnel&lab_id=73&menu_id=263): Design and development of
robotic systems for human use
• Bilge Mutlu's research on human-robot interaction (http://www.bilgemutlu.com/research): Design of social
behavior, understanding the social impact of human-robot interaction
• NASA project on peer-to-peer human-robot interaction (http://www.nasa.gov/multimedia/podcasting/
p2p_robot_vod_transcript.html): Developing tools and methods for human-robot teamwork
• Takayuki Kanda's research on human-robot interaction (http://www.irc.atr.jp/~kanda/research.html):
Experimental research on human-robot communication, field experiments, laboratory studies
• Institute of Robotics and Mechatronics (http://www.dlr.de/rm/en/desktopdefault.aspx/tabid-5471/)
Germany's national research centre for aeronautics and space
• CHRIS project (http://164.11.131.110/) Cooperative Human Robot Interaction Systems is a European project
on HRI research
• Interactions and Communication Design Lab (ICD), Toyohashi University of Technology (http://www.icd.
tutkie.tut.ac.jp/en/profile.html): ICD has been developing a variety of futuristic sociable artifacts, robots, and
creatures in human-centric applications and conducting research on the next generation robot which aims to
establish a communication between people and robots in social interactions.
• The European Aliz-E research project focusing on robot-child interaction (http://www.aliz-e.org): Aliz-E
focuses on the interactions robots can have with children in the long run and in real-life environments, especially
in paediatrics departments.
• The European LOCOBOT research project that is focused on an autonomous robot assistant, capable of speech
and gesture interaction for the industrial environment. (http://www.locobot.eu/)
• Ulrich Hottelet: Albert is not happy - How robots learn to live with people (http://african-times.com/index.
php?option=com_content&view=article&id=2478:albert-is-not-happy&catid=73:june-2009-business&
Itemid=63), African Times (http://african-times.com/), June 2009
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Artificial intelligence
Artificial intelligence (AI) is the intelligence exhibited by machines or software, and the branch of computer
science that develops machines and software with intelligence. Major AI researchers and textbooks define the field
as "the study and design of intelligent agents", where an intelligent agent is a system that perceives its environment
and takes actions that maximize its chances of success. John McCarthy, who coined the term in 1955, defines it as
"the science and engineering of making intelligent machines".
AI research is highly technical and specialised, and is deeply divided into subfields that often fail to communicate
with each other. Some of the division is due to social and cultural factors: subfields have grown up around particular
institutions and the work of individual researchers. AI research is also divided by several technical issues. Some
subfields focus on the solution of specific problems. Others focus on one of several possible approaches or on the
use of a particular tool or towards the accomplishment of particular applications.
The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, communication,
perception and the ability to move and manipulate objects. General intelligence (or "strong AI") is still among the
field's long term goals. Currently popular approaches include statistical methods, computational intelligence and
traditional symbolic AI. There are an enormous number of tools used in AI, including versions of search and
mathematical optimization, logic, methods based on probability and economics, and many others.
The field was founded on the claim that a central property of humans, intelligence—the sapience of Homo
sapiens—can be so precisely described that it can be simulated by a machine.[1] This raises philosophical issues
about the nature of the mind and the ethics of creating artificial beings, issues which have been addressed by myth,
fiction and philosophy since antiquity. Artificial intelligence has been the subject of tremendous optimism[2] but has
also suffered stunning setbacks.[3] Today it has become an essential part of the technology industry and many of the
most difficult problems in computer science.
History
Thinking machines and artificial beings appear in Greek myths, such as Talos of Crete, the bronze robot of
Hephaestus, and Pygmalion's Galatea. Human likenesses believed to have intelligence were built in every major
civilization: animated cult images were worshiped in Egypt and Greece and humanoid automatons were built by Yan
Shi, Hero of Alexandria and Al-Jazari. It was also widely believed that artificial beings had been created by Jābir ibn
Hayyān, Judah Loew and Paracelsus. By the 19th and 20th centuries, artificial beings had become a common feature
in fiction, as in Mary Shelley's Frankenstein or Karel Čapek's R.U.R. (Rossum's Universal Robots). Pamela
McCorduck argues that all of these are examples of an ancient urge, as she describes it, "to forge the gods". Stories
of these creatures and their fates discuss many of the same hopes, fears and ethical concerns that are presented by
artificial intelligence.
Mechanical or "formal" reasoning has been developed by philosophers and mathematicians since antiquity. The
study of logic led directly to the invention of the programmable digital electronic computer, based on the work of
mathematician Alan Turing and others. Turing's theory of computation suggested that a machine, by shuffling
symbols as simple as "0" and "1", could simulate any conceivable act of mathematical deduction.[4] This, along with
concurrent discoveries in neurology, information theory and cybernetics, inspired a small group of researchers to
begin to seriously consider the possibility of building an electronic brain.
The field of AI research was founded at a conference on the campus of Dartmouth College in the summer of 1956.
The attendees, including John McCarthy, Marvin Minsky, Allen Newell and Herbert Simon, became the leaders of
AI research for many decades. They and their students wrote programs that were, to most people, simply
astonishing:[5] Computers were solving word problems in algebra, proving logical theorems and speaking English.
By the middle of the 1960s, research in the U.S. was heavily funded by the Department of Defense and laboratories
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Artificial intelligence
had been established around the world. AI's founders were profoundly optimistic about the future of the new field:
Herbert Simon predicted that "machines will be capable, within twenty years, of doing any work a man can do" and
Marvin Minsky agreed, writing that "within a generation ... the problem of creating 'artificial intelligence' will
substantially be solved".
They had failed to recognize the difficulty of some of the problems they faced.[6] In 1974, in response to the criticism
of Sir James Lighthill and ongoing pressure from the US Congress to fund more productive projects, both the U.S.
and British governments cut off all undirected exploratory research in AI. The next few years would later be called
an "AI winter", a period when funding for AI projects was hard to find.
In the early 1980s, AI research was revived by the commercial success of expert systems, a form of AI program that
simulated the knowledge and analytical skills of one or more human experts. By 1985 the market for AI had reached
over a billion dollars. At the same time, Japan's fifth generation computer project inspired the U.S and British
governments to restore funding for academic research in the field. However, beginning with the collapse of the Lisp
Machine market in 1987, AI once again fell into disrepute, and a second, longer lasting AI winter began.
In the 1990s and early 21st century, AI achieved its greatest successes, albeit somewhat behind the scenes. Artificial
intelligence is used for logistics, data mining, medical diagnosis and many other areas throughout the technology
industry. The success was due to several factors: the increasing computational power of computers (see Moore's
law), a greater emphasis on solving specific subproblems, the creation of new ties between AI and other fields
working on similar problems, and a new commitment by researchers to solid mathematical methods and rigorous
scientific standards.
On 11 May 1997, Deep Blue became the first computer chess-playing system to beat a reigning world chess
champion, Garry Kasparov. In 2005, a Stanford robot won the DARPA Grand Challenge by driving autonomously
for 131 miles along an unrehearsed desert trail.[7] Two years later, a team from CMU won the DARPA Urban
Challenge when their vehicle autonomously navigated 55 miles in an urban environment while adhering to traffic
hazards and all traffic laws. In February 2011, in a Jeopardy! quiz show exhibition match, IBM's question answering
system, Watson, defeated the two greatest Jeopardy champions, Brad Rutter and Ken Jennings, by a significant
margin. The Kinect, which provides a 3D body–motion interface for the Xbox 360 and the Xbox One, uses
algorithms that emerged from lengthy AI research[8] as does the iPhone's Siri.
Goals
The general problem of simulating (or creating) intelligence has been broken down into a number of specific
sub-problems. These consist of particular traits or capabilities that researchers would like an intelligent system to
display. The traits described below have received the most attention.
Deduction, reasoning, problem solving
Early AI researchers developed algorithms that imitated the step-by-step reasoning that humans use when they solve
puzzles or make logical deductions. By the late 1980s and 1990s, AI research had also developed highly successful
methods for dealing with uncertain or incomplete information, employing concepts from probability and economics.
For difficult problems, most of these algorithms can require enormous computational resources – most experience a
"combinatorial explosion": the amount of memory or computer time required becomes astronomical when the
problem goes beyond a certain size. The search for more efficient problem-solving algorithms is a high priority for
AI research.
Human beings solve most of their problems using fast, intuitive judgements rather than the conscious, step-by-step
deduction that early AI research was able to model. AI has made some progress at imitating this kind of
"sub-symbolic" problem solving: embodied agent approaches emphasize the importance of sensorimotor skills to
higher reasoning; neural net research attempts to simulate the structures inside the brain that give rise to this skill;
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69
statistical approaches to AI mimic the probabilistic nature of the human ability to guess.
Knowledge representation
Knowledge representation and knowledge engineering are central to AI
research. Many of the problems machines are expected to solve will
require extensive knowledge about the world. Among the things that
AI needs to represent are: objects, properties, categories and relations
between objects; situations, events, states and time; causes and effects;
knowledge about knowledge (what we know about what other people
know); and many other, less well researched domains. A representation
of "what exists" is an ontology: the set of objects, relations, concepts
and so on that the machine knows about. The most general are called
upper ontologies, which attempt to provide a foundation for all other
knowledge.
Among the most difficult problems in knowledge representation are:
Default reasoning and the qualification problem
An ontology represents knowledge as a set of
concepts within a domain and the relationships
between those concepts.
Many of the things people know take the form of "working assumptions." For example, if a bird comes up in
conversation, people typically picture an animal that is fist sized, sings, and flies. None of these things are true
about all birds. John McCarthy identified this problem in 1969 as the qualification problem: for any
commonsense rule that AI researchers care to represent, there tend to be a huge number of exceptions. Almost
nothing is simply true or false in the way that abstract logic requires. AI research has explored a number of
solutions to this problem.
The breadth of commonsense knowledge
The number of atomic facts that the average person knows is astronomical. Research projects that attempt to
build a complete knowledge base of commonsense knowledge (e.g., Cyc) require enormous amounts of
laborious ontological engineering — they must be built, by hand, one complicated concept at a time. A major
goal is to have the computer understand enough concepts to be able to learn by reading from sources like the
internet, and thus be able to add to its own ontology.[citation needed]
The subsymbolic form of some commonsense knowledge
Much of what people know is not represented as "facts" or "statements" that they could express verbally. For
example, a chess master will avoid a particular chess position because it "feels too exposed" or an art critic can
take one look at a statue and instantly realize that it is a fake. These are intuitions or tendencies that are
represented in the brain non-consciously and sub-symbolically. Knowledge like this informs, supports and
provides a context for symbolic, conscious knowledge. As with the related problem of sub-symbolic
reasoning, it is hoped that situated AI, computational intelligence, or statistical AI will provide ways to
represent this kind of knowledge.
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70
Planning
Intelligent agents must be able to set goals and achieve them. They
need a way to visualize the future (they must have a representation of
the state of the world and be able to make predictions about how their
actions will change it) and be able to make choices that maximize the
utility (or "value") of the available choices.
In classical planning problems, the agent can assume that it is the only
thing acting on the world and it can be certain what the consequences
of its actions may be. However, if the agent is not the only actor, it
must periodically ascertain whether the world matches its predictions
and it must change its plan as this becomes necessary, requiring the
agent to reason under uncertainty.
Multi-agent planning uses the cooperation and competition of many
agents to achieve a given goal. Emergent behavior such as this is used
by evolutionary algorithms and swarm intelligence.
A hierarchical control system is a form of control
system in which a set of devices and governing
software is arranged in a hierarchy.
Learning
Machine learning is the study of computer algorithms that improve automatically through experience[9] and has been
central to AI research since the field's inception.[10]
Unsupervised learning is the ability to find patterns in a stream of input. Supervised learning includes both
classification and numerical regression. Classification is used to determine what category something belongs in, after
seeing a number of examples of things from several categories. Regression is the attempt to produce a function that
describes the relationship between inputs and outputs and predicts how the outputs should change as the inputs
change. In reinforcement learning the agent is rewarded for good responses and punished for bad ones. These can be
analyzed in terms of decision theory, using concepts like utility. The mathematical analysis of machine learning
algorithms and their performance is a branch of theoretical computer science known as computational learning
theory.
Within developmental robotics, developmental learning approaches were elaborated for lifelong cumulative
acquisition of repertoires of novel skills by a robot, through autonomous self-exploration and social interaction with
human teachers, and using guidance mechanisms such as active learning, maturation, motor synergies, and
imitation.[11][12][13][14]
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71
Natural language processing
Natural language processing gives machines the ability to read and
understand the languages that humans speak. A sufficiently powerful
natural language processing system would enable natural language user
interfaces and the acquisition of knowledge directly from
human-written sources, such as Internet texts. Some straightforward
applications of natural language processing include information
retrieval (or text mining) and machine translation.
A common method of processing and extracting meaning from natural
language is through semantic indexing. Increases in processing speeds
and the drop in the cost of data storage makes indexing large volumes
of abstractions of the users input much more efficient.
Motion and manipulation
A parse tree represents the syntactic structure of a
sentence according to some formal grammar.
The field of robotics is closely related to AI. Intelligence is required for robots to be able to handle such tasks as
object manipulation and navigation, with sub-problems of localization (knowing where you are, or finding out where
other things are), mapping (learning what is around you, building a map of the environment), and motion planning
(figuring out how to get there) or path planning (going from one point in space to another point, which may involve
compliant motion - where the robot moves while maintaining physical contact with an object).
Perception
Machine perception is the ability to use input from sensors (such as cameras, microphones, sonar and others more
exotic) to deduce aspects of the world. Computer vision is the ability to analyze visual input. A few selected
subproblems are speech recognition, facial recognition and object recognition.
Social intelligence
Affective computing is the study and development of systems and
devices that can recognize, interpret, process, and simulate human
affects. It is an interdisciplinary field spanning computer sciences,
psychology, and cognitive science. While the origins of the field may
be traced as far back as to early philosophical inquiries into
emotion,[15] the more modern branch of computer science originated
with Rosalind Picard's 1995 paper[16] on affective computing. A
motivation for the research is the ability to simulate empathy. The
machine should interpret the emotional state of humans and adapt its
behaviour to them, giving an appropriate response for those emotions.
Kismet, a robot with rudimentary social skills
Emotion and social skills play two roles for an intelligent agent. First, it must be able to predict the actions of others,
by understanding their motives and emotional states. (This involves elements of game theory, decision theory, as
well as the ability to model human emotions and the perceptual skills to detect emotions.) Also, in an effort to
facilitate human-computer interaction, an intelligent machine might want to be able to display emotions—even if it
does not actually experience them itself—in order to appear sensitive to the emotional dynamics of human
interaction.
Artificial intelligence
Creativity
A sub-field of AI addresses creativity both theoretically (from a philosophical and psychological perspective) and
practically (via specific implementations of systems that generate outputs that can be considered creative, or systems
that identify and assess creativity). Related areas of computational research are Artificial intuition and Artificial
imagination.
General intelligence
Most researchers think that their work will eventually be incorporated into a machine with general intelligence
(known as strong AI), combining all the skills above and exceeding human abilities at most or all of them. A few
believe that anthropomorphic features like artificial consciousness or an artificial brain may be required for such a
project.
Many of the problems above may require general intelligence to be considered solved. For example, even a
straightforward, specific task like machine translation requires that the machine read and write in both languages
(NLP), follow the author's argument (reason), know what is being talked about (knowledge), and faithfully
reproduce the author's intention (social intelligence). A problem like machine translation is considered
"AI-complete". In order to solve this particular problem, you must solve all the problems.
Approaches
There is no established unifying theory or paradigm that guides AI research. Researchers disagree about many
issues.[17] A few of the most long standing questions that have remained unanswered are these: should artificial
intelligence simulate natural intelligence by studying psychology or neurology? Or is human biology as irrelevant to
AI research as bird biology is to aeronautical engineering? Can intelligent behavior be described using simple,
elegant principles (such as logic or optimization)? Or does it necessarily require solving a large number of
completely unrelated problems? Can intelligence be reproduced using high-level symbols, similar to words and
ideas? Or does it require "sub-symbolic" processing? John Haugeland, who coined the term GOFAI (Good
Old-Fashioned Artificial Intelligence), also proposed that AI should more properly be referred to as synthetic
intelligence,[18] a term which has since been adopted by some non-GOFAI researchers.[19]
Cybernetics and brain simulation
In the 1940s and 1950s, a number of researchers explored the connection between neurology, information theory,
and cybernetics. Some of them built machines that used electronic networks to exhibit rudimentary intelligence, such
as W. Grey Walter's turtles and the Johns Hopkins Beast. Many of these researchers gathered for meetings of the
Teleological Society at Princeton University and the Ratio Club in England. By 1960, this approach was largely
abandoned, although elements of it would be revived in the 1980s.
Symbolic
When access to digital computers became possible in the middle 1950s, AI research began to explore the possibility
that human intelligence could be reduced to symbol manipulation. The research was centered in three institutions:
Carnegie Mellon University, Stanford and MIT, and each one developed its own style of research. John Haugeland
named these approaches to AI "good old fashioned AI" or "GOFAI". During the 1960s, symbolic approaches had
achieved great success at simulating high-level thinking in small demonstration programs. Approaches based on
cybernetics or neural networks were abandoned or pushed into the background.[20] Researchers in the 1960s and the
1970s were convinced that symbolic approaches would eventually succeed in creating a machine with artificial
general intelligence and considered this the goal of their field.
Cognitive simulation
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Artificial intelligence
Economist Herbert Simon and Allen Newell studied human problem-solving skills and attempted to formalize
them, and their work laid the foundations of the field of artificial intelligence, as well as cognitive science,
operations research and management science. Their research team used the results of psychological
experiments to develop programs that simulated the techniques that people used to solve problems. This
tradition, centered at Carnegie Mellon University would eventually culminate in the development of the Soar
architecture in the middle 1980s.
Logic-based
Unlike Newell and Simon, John McCarthy felt that machines did not need to simulate human thought, but
should instead try to find the essence of abstract reasoning and problem solving, regardless of whether people
used the same algorithms. His laboratory at Stanford (SAIL) focused on using formal logic to solve a wide
variety of problems, including knowledge representation, planning and learning. Logic was also the focus of
the work at the University of Edinburgh and elsewhere in Europe which led to the development of the
programming language Prolog and the science of logic programming.
"Anti-logic" or "scruffy"
Researchers at MIT (such as Marvin Minsky and Seymour Papert) found that solving difficult problems in
vision and natural language processing required ad-hoc solutions – they argued that there was no simple and
general principle (like logic) that would capture all the aspects of intelligent behavior. Roger Schank described
their "anti-logic" approaches as "scruffy" (as opposed to the "neat" paradigms at CMU and Stanford).
Commonsense knowledge bases (such as Doug Lenat's Cyc) are an example of "scruffy" AI, since they must
be built by hand, one complicated concept at a time.
Knowledge-based
When computers with large memories became available around 1970, researchers from all three traditions
began to build knowledge into AI applications. This "knowledge revolution" led to the development and
deployment of expert systems (introduced by Edward Feigenbaum), the first truly successful form of AI
software. The knowledge revolution was also driven by the realization that enormous amounts of knowledge
would be required by many simple AI applications.
Sub-symbolic
By the 1980s progress in symbolic AI seemed to stall and many believed that symbolic systems would never be able
to imitate all the processes of human cognition, especially perception, robotics, learning and pattern recognition. A
number of researchers began to look into "sub-symbolic" approaches to specific AI problems.
Bottom-up, embodied, situated, behavior-based or nouvelle AI
Researchers from the related field of robotics, such as Rodney Brooks, rejected symbolic AI and focused on
the basic engineering problems that would allow robots to move and survive. Their work revived the
non-symbolic viewpoint of the early cybernetics researchers of the 1950s and reintroduced the use of control
theory in AI. This coincided with the development of the embodied mind thesis in the related field of cognitive
science: the idea that aspects of the body (such as movement, perception and visualization) are required for
higher intelligence.
Computational intelligence
Interest in neural networks and "connectionism" was revived by David Rumelhart and others in the middle
1980s. These and other sub-symbolic approaches, such as fuzzy systems and evolutionary computation, are
now studied collectively by the emerging discipline of computational intelligence.
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Statistical
In the 1990s, AI researchers developed sophisticated mathematical tools to solve specific subproblems. These tools
are truly scientific, in the sense that their results are both measurable and verifiable, and they have been responsible
for many of AI's recent successes. The shared mathematical language has also permitted a high level of collaboration
with more established fields (like mathematics, economics or operations research). Stuart Russell and Peter Norvig
describe this movement as nothing less than a "revolution" and "the victory of the neats." Critics argue that these
techniques are too focused on particular problems and have failed to address the long term goal of general
intelligence. There is an ongoing debate about the relevance and validity of statistical approaches in AI, exemplified
in part by exchanges between Peter Norvig and Noam Chomsky.[21][22]
Integrating the approaches
Intelligent agent paradigm
An intelligent agent is a system that perceives its environment and takes actions which maximize its chances
of success. The simplest intelligent agents are programs that solve specific problems. More complicated agents
include human beings and organizations of human beings (such as firms). The paradigm gives researchers
license to study isolated problems and find solutions that are both verifiable and useful, without agreeing on
one single approach. An agent that solves a specific problem can use any approach that works – some agents
are symbolic and logical, some are sub-symbolic neural networks and others may use new approaches. The
paradigm also gives researchers a common language to communicate with other fields—such as decision
theory and economics—that also use concepts of abstract agents. The intelligent agent paradigm became
widely accepted during the 1990s.
Agent architectures and cognitive architectures
Researchers have designed systems to build intelligent systems out of interacting intelligent agents in a
multi-agent system. A system with both symbolic and sub-symbolic components is a hybrid intelligent system,
and the study of such systems is artificial intelligence systems integration. A hierarchical control system
provides a bridge between sub-symbolic AI at its lowest, reactive levels and traditional symbolic AI at its
highest levels, where relaxed time constraints permit planning and world modelling. Rodney Brooks'
subsumption architecture was an early proposal for such a hierarchical system.
Tools
In the course of 50 years of research, AI has developed a large number of tools to solve the most difficult problems
in computer science. A few of the most general of these methods are discussed below.
Search and optimization
Many problems in AI can be solved in theory by intelligently searching through many possible solutions: Reasoning
can be reduced to performing a search. For example, logical proof can be viewed as searching for a path that leads
from premises to conclusions, where each step is the application of an inference rule. Planning algorithms search
through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis.
Robotics algorithms for moving limbs and grasping objects use local searches in configuration space. Many learning
algorithms use search algorithms based on optimization.
Simple exhaustive searches are rarely sufficient for most real world problems: the search space (the number of places
to search) quickly grows to astronomical numbers. The result is a search that is too slow or never completes. The
solution, for many problems, is to use "heuristics" or "rules of thumb" that eliminate choices that are unlikely to lead
to the goal (called "pruning the search tree"). Heuristics supply the program with a "best guess" for the path on which
the solution lies. Heuristics limit the search for solutions into a smaller sample size.
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Artificial intelligence
A very different kind of search came to prominence in the 1990s, based on the mathematical theory of optimization.
For many problems, it is possible to begin the search with some form of a guess and then refine the guess
incrementally until no more refinements can be made. These algorithms can be visualized as blind hill climbing: we
begin the search at a random point on the landscape, and then, by jumps or steps, we keep moving our guess uphill,
until we reach the top. Other optimization algorithms are simulated annealing, beam search and random
optimization.
Evolutionary computation uses a form of optimization search. For example, they may begin with a population of
organisms (the guesses) and then allow them to mutate and recombine, selecting only the fittest to survive each
generation (refining the guesses). Forms of evolutionary computation include swarm intelligence algorithms (such as
ant colony or particle swarm optimization) and evolutionary algorithms (such as genetic algorithms, gene expression
programming, and genetic programming).
Logic
Logic is used for knowledge representation and problem solving, but it can be applied to other problems as well. For
example, the satplan algorithm uses logic for planning and inductive logic programming is a method for learning.
Several different forms of logic are used in AI research. Propositional or sentential logic is the logic of statements
which can be true or false. First-order logic also allows the use of quantifiers and predicates, and can express facts
about objects, their properties, and their relations with each other. Fuzzy logic, is a version of first-order logic which
allows the truth of a statement to be represented as a value between 0 and 1, rather than simply True (1) or False (0).
Fuzzy systems can be used for uncertain reasoning and have been widely used in modern industrial and consumer
product control systems. Subjective logic models uncertainty in a different and more explicit manner than
fuzzy-logic: a given binomial opinion satisfies belief + disbelief + uncertainty = 1 within a Beta distribution. By this
method, ignorance can be distinguished from probabilistic statements that an agent makes with high confidence.
Default logics, non-monotonic logics and circumscription are forms of logic designed to help with default reasoning
and the qualification problem. Several extensions of logic have been designed to handle specific domains of
knowledge, such as: description logics; situation calculus, event calculus and fluent calculus (for representing events
and time); causal calculus; belief calculus; and modal logics.
Probabilistic methods for uncertain reasoning
Many problems in AI (in reasoning, planning, learning, perception and robotics) require the agent to operate with
incomplete or uncertain information. AI researchers have devised a number of powerful tools to solve these
problems using methods from probability theory and economics.
Bayesian networks are a very general tool that can be used for a large number of problems: reasoning (using the
Bayesian inference algorithm), learning (using the expectation-maximization algorithm), planning (using decision
networks) and perception (using dynamic Bayesian networks). Probabilistic algorithms can also be used for filtering,
prediction, smoothing and finding explanations for streams of data, helping perception systems to analyze processes
that occur over time (e.g., hidden Markov models or Kalman filters).
A key concept from the science of economics is "utility": a measure of how valuable something is to an intelligent
agent. Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using
decision theory, decision analysis, information value theory. These tools include models such as Markov decision
processes, dynamic decision networks, game theory and mechanism design.
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76
Classifiers and statistical learning methods
The simplest AI applications can be divided into two types: classifiers ("if shiny then diamond") and controllers ("if
shiny then pick up"). Controllers do however also classify conditions before inferring actions, and therefore
classification forms a central part of many AI systems. Classifiers are functions that use pattern matching to
determine a closest match. They can be tuned according to examples, making them very attractive for use in AI.
These examples are known as observations or patterns. In supervised learning, each pattern belongs to a certain
predefined class. A class can be seen as a decision that has to be made. All the observations combined with their
class labels are known as a data set. When a new observation is received, that observation is classified based on
previous experience.
A classifier can be trained in various ways; there are many statistical and machine learning approaches. The most
widely used classifiers are the neural network, kernel methods such as the support vector machine, k-nearest
neighbor algorithm, Gaussian mixture model, naive Bayes classifier, and decision tree. The performance of these
classifiers have been compared over a wide range of tasks. Classifier performance depends greatly on the
characteristics of the data to be classified. There is no single classifier that works best on all given problems; this is
also referred to as the "no free lunch" theorem. Determining a suitable classifier for a given problem is still more an
art than science.
Neural networks
The study of artificial neural networks began in the decade before the
field AI research was founded, in the work of Walter Pitts and Warren
McCullough. Other important early researchers were Frank Rosenblatt,
who invented the perceptron and Paul Werbos who developed the
backpropagation algorithm.
The main categories of networks are acyclic or feedforward neural
networks (where the signal passes in only one direction) and recurrent
neural networks (which allow feedback). Among the most popular
feedforward networks are perceptrons, multi-layer perceptrons and
radial basis networks. Among recurrent networks, the most famous is
the Hopfield net, a form of attractor network, which was first described
by John Hopfield in 1982. Neural networks can be applied to the
problem of intelligent control (for robotics) or learning, using such
techniques as Hebbian learning and competitive learning.
A neural network is an interconnected group of
nodes, akin to the vast network of neurons in the
human brain.
Hierarchical temporal memory is an approach that models some of the structural and algorithmic properties of the
neocortex.
Control theory
Control theory, the grandchild of cybernetics, has many important applications, especially in robotics.
Languages
AI researchers have developed several specialized languages for AI research, including Lisp and Prolog.
Evaluating progress
In 1950, Alan Turing proposed a general procedure to test the intelligence of an agent now known as the Turing test.
This procedure allows almost all the major problems of artificial intelligence to be tested. However, it is a very
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77
difficult challenge and at present all agents fail.
Artificial intelligence can also be evaluated on specific problems such as small problems in chemistry, hand-writing
recognition and game-playing. Such tests have been termed subject matter expert Turing tests. Smaller problems
provide more achievable goals and there are an ever-increasing number of positive results.
One classification for outcomes of an AI test is:
1.
2.
3.
4.
Optimal: it is not possible to perform better.
Strong super-human: performs better than all humans.
Super-human: performs better than most humans.
Sub-human: performs worse than most humans.
For example, performance at draughts (i.e. checkers) is optimal, performance at chess is super-human and nearing
strong super-human (see computer chess: computers versus human) and performance at many everyday tasks (such
as recognizing a face or crossing a room without bumping into something) is sub-human.
A quite different approach measures machine intelligence through tests which are developed from mathematical
definitions of intelligence. Examples of these kinds of tests start in the late nineties devising intelligence tests using
notions from Kolmogorov complexity and data compression. Two major advantages of mathematical definitions are
their applicability to nonhuman intelligences and their absence of a requirement for human testers.
An area that artificial intelligence had contributed greatly to is Intrusion detection.
A derivative of the Turing test is the Completely Automated Public Turing test to tell Computers and Humans Apart
(CAPTCHA). as the name implies, this helps to determine that a user is an actual person and not a computer posing
as a human. In contrast to the standard Turing test, CAPTCHA administered by a machine and targeted to a human
as opposed to being administered by a human and targeted to a machine. A computer asks a user to complete a
simple test then generates a grade for that test. Computers are unable to solve the problem, so correct solutions are
deemed to be the result of a person taking the test. A common type of CAPTCHA is the test that requires the typing
of distorted letters, numbers or symbols that appear in an image undecipherable by a computer.[23]
Applications
Artificial intelligence techniques are pervasive and are too numerous to
list. Frequently, when a technique reaches mainstream use, it is no
longer considered artificial intelligence; this phenomenon is described
as the AI effect.
Competitions and prizes
There are a number of competitions and prizes to promote research in
artificial intelligence. The main areas promoted are: general machine
intelligence, conversational behavior, data-mining, robotic cars, robot
soccer and games.
Platforms
A platform (or "computing platform") is defined as "some sort of
hardware architecture or software framework (including application
frameworks), that allows software to run." As Rodney Brooks[24]
pointed out many years ago, it is not just the artificial intelligence
An automated online assistant providing
customer service on a web page – one of many
very primitive applications of artificial
intelligence.
Artificial intelligence
software that defines the AI features of the platform, but rather the actual platform itself that affects the AI that
results, i.e., there needs to be work in AI problems on real-world platforms rather than in isolation.
A wide variety of platforms has allowed different aspects of AI to develop, ranging from expert systems, albeit
PC-based but still an entire real-world system, to various robot platforms such as the widely available Roomba with
open interface.[25]
Philosophy
Artificial intelligence, by claiming to be able to recreate the capabilities of the human mind, is both a challenge and
an inspiration for philosophy. Are there limits to how intelligent machines can be? Is there an essential difference
between human intelligence and artificial intelligence? Can a machine have a mind and consciousness? A few of the
most influential answers to these questions are given below.
Turing's "polite convention"
We need not decide if a machine can "think"; we need only decide if a machine can act as intelligently as a
human being. This approach to the philosophical problems associated with artificial intelligence forms the
basis of the Turing test.
The Dartmouth proposal
"Every aspect of learning or any other feature of intelligence can be so precisely described that a machine can
be made to simulate it." This conjecture was printed in the proposal for the Dartmouth Conference of 1956,
and represents the position of most working AI researchers.
Newell and Simon's physical symbol system hypothesis
"A physical symbol system has the necessary and sufficient means of general intelligent action." Newell and
Simon argue that intelligences consist of formal operations on symbols. Hubert Dreyfus argued that, on the
contrary, human expertise depends on unconscious instinct rather than conscious symbol manipulation and on
having a "feel" for the situation rather than explicit symbolic knowledge. (See Dreyfus' critique of AI.)[26]
Gödel's incompleteness theorem
A formal system (such as a computer program) cannot prove all true statements.[27] Roger Penrose is among
those who claim that Gödel's theorem limits what machines can do. (See The Emperor's New Mind.)
Searle's strong AI hypothesis
"The appropriately programmed computer with the right inputs and outputs would thereby have a mind in
exactly the same sense human beings have minds." John Searle counters this assertion with his Chinese room
argument, which asks us to look inside the computer and try to find where the "mind" might be.
The artificial brain argument
The brain can be simulated. Hans Moravec, Ray Kurzweil and others have argued that it is technologically
feasible to copy the brain directly into hardware and software, and that such a simulation will be essentially
identical to the original.
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Artificial intelligence
Predictions and ethics
Artificial intelligence is a common topic in both science fiction and projections about the future of technology and
society. The existence of an artificial intelligence that rivals human intelligence raises difficult ethical issues, and the
potential power of the technology inspires both hopes and fears.
In fiction, artificial intelligence has appeared fulfilling many roles.
These include:
•
•
•
•
•
•
•
•
•
•
a servant (R2-D2 and C-3PO in Star Wars)
a law enforcer (K.I.T.T. "Knight Rider")
a comrade (Lt. Commander Data in Star Trek: The Next Generation)
a conqueror/overlord (The Matrix, Omnius)
a dictator (With Folded Hands),(Colossus: The Forbin Project (1970 Movie).
a benevolent provider/de facto ruler (The Culture)
a supercomputer (The Red Queen in Resident Evil "Gilium" in Outlaw Star)
an assassin (Terminator)
a sentient race (Battlestar Galactica/Transformers/Mass Effect)
an extension to human abilities (Ghost in the Shell)
• the savior of the human race (R. Daneel Olivaw in Isaac Asimov's Robot series)
Mary Shelley's Frankenstein considers a key issue in the ethics of artificial intelligence: if a machine can be created
that has intelligence, could it also feel? If it can feel, does it have the same rights as a human? The idea also appears
in modern science fiction, including the films I Robot, Blade Runner and A.I.: Artificial Intelligence, in which
humanoid machines have the ability to feel human emotions. This issue, now known as "robot rights", is currently
being considered by, for example, California's Institute for the Future, although many critics believe that the
discussion is premature. The subject is profoundly discussed in the 2010 documentary film Plug & Pray.[28]
Martin Ford, author of The Lights in the Tunnel: Automation, Accelerating Technology and the Economy of the
Future, and others argue that specialized artificial intelligence applications, robotics and other forms of automation
will ultimately result in significant unemployment as machines begin to match and exceed the capability of workers
to perform most routine and repetitive jobs. Ford predicts that many knowledge-based occupations—and in
particular entry level jobs—will be increasingly susceptible to automation via expert systems, machine learning[29]
and other AI-enhanced applications. AI-based applications may also be used to amplify the capabilities of low-wage
offshore workers, making it more feasible to outsource knowledge work.
Joseph Weizenbaum wrote that AI applications can not, by definition, successfully simulate genuine human empathy
and that the use of AI technology in fields such as customer service or psychotherapy[30] was deeply misguided.
Weizenbaum was also bothered that AI researchers (and some philosophers) were willing to view the human mind as
nothing more than a computer program (a position now known as computationalism). To Weizenbaum these points
suggest that AI research devalues human life.
Many futurists believe that artificial intelligence will ultimately transcend the limits of progress. Ray Kurzweil has
used Moore's law (which describes the relentless exponential improvement in digital technology) to calculate that
desktop computers will have the same processing power as human brains by the year 2029. He also predicts that by
2045 artificial intelligence will reach a point where it is able to improve itself at a rate that far exceeds anything
conceivable in the past, a scenario that science fiction writer Vernor Vinge named the "singularity".
Robot designer Hans Moravec, cyberneticist Kevin Warwick and inventor Ray Kurzweil have predicted that humans
and machines will merge in the future into cyborgs that are more capable and powerful than either. This idea, called
transhumanism, which has roots in Aldous Huxley and Robert Ettinger, has been illustrated in fiction as well, for
example in the manga Ghost in the Shell and the science-fiction series Dune. In the 1980s artist Hajime Sorayama's
Sexy Robots series were painted and published in Japan depicting the actual organic human form with life-like
79
Artificial intelligence
muscular metallic skins and later "the Gynoids" book followed that was used by or influenced movie makers
including George Lucas and other creatives. Sorayama never considered these organic robots to be real part of nature
but always unnatural product of the human mind, a fantasy existing in the mind even when realized in actual form.
Almost 20 years later, the first AI robotic pet, AIBO, came available as a companion to people. AIBO grew out of
Sony's Computer Science Laboratory (CSL). Famed engineer Toshitada Doi is credited as AIBO's original
progenitor: in 1994 he had started work on robots with artificial intelligence expert Masahiro Fujita, at CSL. Doi's,
friend, the artist Hajime Sorayama, was enlisted to create the initial designs for the AIBO's body. Those designs are
now part of the permanent collections of Museum of Modern Art and the Smithsonian Institution, with later versions
of AIBO being used in studies in Carnegie Mellon University. In 2006, AIBO was added into Carnegie Mellon
University's "Robot Hall of Fame".
Political scientist Charles T. Rubin believes that AI can be neither designed nor guaranteed to be friendly. He argues
that "any sufficiently advanced benevolence may be indistinguishable from malevolence." Humans should not
assume machines or robots would treat us favorably, because there is no a priori reason to believe that they would be
sympathetic to our system of morality, which has evolved along with our particular biology (which AIs would not
share).
Edward Fredkin argues that "artificial intelligence is the next stage in evolution", an idea first proposed by Samuel
Butler's "Darwin among the Machines" (1863), and expanded upon by George Dyson in his book of the same name
in 1998.
References
Notes
[1] See the Dartmouth proposal, under Philosophy, below.
[2] The optimism referred to includes the predictions of early AI researchers (see optimism in the history of AI) as well as the ideas of modern
transhumanists such as Ray Kurzweil.
[3] The "setbacks" referred to include the ALPAC report of 1966, the abandonment of perceptrons in 1970, the Lighthill Report of 1973 and the
collapse of the Lisp machine market in 1987.
[4] This insight, that digital computers can simulate any process of formal reasoning, is known as the Church–Turing thesis.
[5] Russell and Norvig write "it was astonishing whenever a computer did anything kind of smartish."
[6] See
[7] DARPA Grand Challenge – home page (http:/ / www. darpa. mil/ grandchallenge/ )
[8] Kinect's AI breakthrough explained (http:/ / www. i-programmer. info/ news/ 105-artificial-intelligence/
2176-kinects-ai-breakthrough-explained. html)
[9] This is a form of Tom Mitchell's widely quoted definition of machine learning: "A computer program is set to learn from an experience E
with respect to some task T and some performance measure P if its performance on T as measured by P improves with experience E."
[10] Alan Turing discussed the centrality of learning as early as 1950, in his classic paper "Computing Machinery and Intelligence". In 1956, at
the original Dartmouth AI summer conference, Ray Solomonoff wrote a report on unsupervised probabilistic machine learning: "An Inductive
Inference Machine". (pdf scanned copy of the original) (http:/ / world. std. com/ ~rjs/ indinf56. pdf) (version published in 1957, An Inductive
Inference Machine," IRE Convention Record, Section on Information Theory, Part 2, pp. 56–62)
[11] Weng, J., McClelland, Pentland, A.,Sporns, O., Stockman, I., Sur, M., and E. Thelen (2001) "Autonomous mental development by robots
and animals" (http:/ / www. cse. msu. edu/ dl/ SciencePaper. pdf), Science, vol. 291, pp. 599–600.
[12] Lungarella, M., Metta, G., Pfeifer, R. and G. Sandini (2003). "Developmental robotics: a survey" (http:/ / citeseerx. ist. psu. edu/ viewdoc/
download?doi=10. 1. 1. 83. 7615& rep=rep1& type=pdf). Connection Science, 15:151–190.
[13] Asada, M., Hosoda, K., Kuniyoshi, Y., Ishiguro, H., Inui, T., Yoshikawa, Y., Ogino, M. and C. Yoshida (2009) "Cognitive developmental
robotics: a survey" (http:/ / ieeexplore. ieee. org/ xpl/ login. jsp?tp=& arnumber=4895715& url=http:/ / ieeexplore. ieee. org/ iel5/ 4563672/
5038478/ 04895715. pdf?arnumber=4895715). IEEE Transactions on Autonomous Mental Development, Vol.1, No.1, pp.12--34.
[14] Oudeyer, P-Y. (2010) "On the impact of robotics in behavioral and cognitive sciences: from insect navigation to human cognitive
development" (http:/ / www. pyoudeyer. com/ IEEETAMDOudeyer10. pdf), IEEE Transactions on Autonomous Mental Development, 2(1),
pp. 2--16.
[15] Cited by Tao and Tan.
[16] "Affective Computing" (http:/ / affect. media. mit. edu/ pdfs/ 95. picard. pdf) MIT Technical Report #321 ( Abstract (http:/ / vismod. media.
mit. edu/ pub/ tech-reports/ TR-321-ABSTRACT. html)), 1995
[17] Nils Nilsson writes: "Simply put, there is wide disagreement in the field about what AI is all about" .
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Artificial intelligence
[18] Haugeland 1985, p. 255.
[19] http:/ / citeseerx. ist. psu. edu/ viewdoc/ download?doi=10. 1. 1. 38. 8384& rep=rep1& type=pdf
[20] The most dramatic case of sub-symbolic AI being pushed into the background was the devastating critique of perceptrons by Marvin Minsky
and Seymour Papert in 1969. See History of AI, AI winter, or Frank Rosenblatt.
[21] Yarden Katz, "Noam Chomsky on Where Artificial Intelligence Went Wrong" (http:/ / www. theatlantic. com/ technology/ archive/ 2012/
11/ noam-chomsky-on-where-artificial-intelligence-went-wrong/ 261637/ ?single_page=true), The Atlantic, November 1, 2012
[22] Peter Norvig, "On Chomsky and the Two Cultures of Statistical Learning" (http:/ / norvig. com/ chomsky. html)
[23] O'Brien and Marakas, 2011, Management Information Systems 10th ed.
[24] Brooks, R.A., "How to build complete creatures rather than isolated cognitive simulators," in K. VanLehn (ed.), Architectures for
Intelligence, pp. 225–239, Lawrence Erlbaum Associates, Hillsdale, NJ, 1991.
[25] Hacking Roomba » Search Results » atmel (http:/ / hackingroomba. com/ ?s=atmel)
[26] Dreyfus criticized the necessary condition of the physical symbol system hypothesis, which he called the "psychological assumption": "The
mind can be viewed as a device operating on bits of information according to formal rules".
[27] This is a paraphrase of the relevant implication of Gödel's theorems.
[28] Independent documentary Plug & Pray, featuring Joseph Weizenbaum and Raymond Kurzweil (http:/ / www. plugandpray-film. de/ en/
content. html)
[29] "Machine Learning: A Job Killer?" (http:/ / econfuture. wordpress. com/ 2011/ 04/ 14/ machine-learning-a-job-killer/ )
[30] In the early 1970s, Kenneth Colby presented a version of Weizenbaum's ELIZA known as DOCTOR which he promoted as a serious
therapeutic tool.
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• van der Walt, Christiaan; Bernard, Etienne (2006<!––year is presumed based on acknowledgements at the end of
the article––>). "Data characteristics that determine classifier performance" (http://www.patternrecognition.co.
za/publications/cvdwalt_data_characteristics_classifiers.pdf) (PDF). Retrieved 5 August 2009.
• Vinge, Vernor (1993). "The Coming Technological Singularity: How to Survive in the Post-Human Era" (http://
www-rohan.sdsu.edu/faculty/vinge/misc/singularity.html).
• Wason, P. C.; Shapiro, D. (1966). "Reasoning". In Foss, B. M. New horizons in psychology. Harmondsworth:
Penguin.
• Weizenbaum, Joseph (1976). Computer Power and Human Reason. San Francisco: W.H. Freeman & Company.
ISBN 0-7167-0464-1.
• Kumar, Gulshan; Krishan Kumar (2012). "The Use of Artificial-Intelligence-Based Ensembles for Intrusion
Detection: A Review" (http://www.hindawi.com.proxy.lib.umich.edu/journals/acisc/2012/850160/).
Applied Computational Intelligence and Soft Computing 2012: 1–20. doi: 10.1155/2012/850160 (http://dx.doi.
org/10.1155/2012/850160). Retrieved 11 February 2013.
Further reading
• TechCast Article Series, John Sagi, Framing Consciousness (http://www.techcast.org/Upload/PDFs/
634146249446122137_Consciousness-Sagifinalversion.pdf)
• Boden, Margaret, Mind As Machine, Oxford University Press, 2006
• Johnston, John (2008) "The Allure of Machinic Life: Cybernetics, Artificial Life, and the New AI", MIT Press
• Myers, Courtney Boyd ed. (2009). The AI Report (http://www.forbes.com/2009/06/22/
singularity-robots-computers-opinions-contributors-artificial-intelligence-09_land.html). Forbes June 2009
• Serenko, Alexander (2010). "The development of an AI journal ranking based on the revealed preference
approach" (http://www.aserenko.com/papers/JOI_Serenko_AI_Journal_Ranking_Published.pdf) (PDF).
Journal of Informetrics 4 (4): 447–459. doi: 10.1016/j.joi.2010.04.001 (http://dx.doi.org/10.1016/j.joi.2010.
04.001).
• Serenko, Alexander; Michael Dohan (2011). "Comparing the expert survey and citation impact journal ranking
methods: Example from the field of Artificial Intelligence" (http://www.aserenko.com/papers/
84
Artificial intelligence
85
JOI_AI_Journal_Ranking_Serenko.pdf) (PDF). Journal of Informetrics 5 (4): 629–649. doi:
10.1016/j.joi.2011.06.002 (http://dx.doi.org/10.1016/j.joi.2011.06.002).
• Sun, R. & Bookman, L. (eds.), Computational Architectures: Integrating Neural and Symbolic Processes. Kluwer
Academic Publishers, Needham, MA. 1994.
External links
• What Is AI? (http://www-formal.stanford.edu/jmc/whatisai/whatisai.html) — An introduction to artificial
intelligence by AI founder John McCarthy.
• The Handbook of Artificial Intelligence Volume Ⅰ by Avron Barr and Edward A. Feigenbaum (Stanford
University) (http://archive.org/details/handbookofartific01barr/)
• Artificial Intelligence (http://www.iep.utm.edu/art-inte) entry in the Internet Encyclopedia of Philosophy
• Logic and Artificial Intelligence (http://plato.stanford.edu/entries/logic-ai) entry by Richmond Thomason in
the Stanford Encyclopedia of Philosophy
• AI (http://www.dmoz.org/Computers/Artificial_Intelligence//) on the Open Directory Project
• AITopics (http://aaai.org/AITopics/) — A large directory of links and other resources maintained by the
Association for the Advancement of Artificial Intelligence, the leading organization of academic AI researchers.
Outline of robotics
Robotics
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Outline
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History
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Glossary
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Index
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Portal
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Category
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Robotics – branch of technology that deals with the design, construction, operation, structural disposition,
manufacture and application of robots. Robotics is related to the sciences of electronics, engineering, mechanics, and
software. The word "robot" was introduced to the public by Czech writer Karel Čapek in his play R.U.R. (Rossum's
Universal Robots), published in 1920. The term "robotics" was coined by Isaac Asimov in his 1941 science fiction
short-story "Liar!"[2]
Outline of robotics
Nature of robotics
Robotics can be described as:
• An applied science – scientific knowledge transferred into a physical environment.
• Research and development –
• A branch of technology –
Branches of robotics
Robotics incorporates aspects of many disciplines including electronics, engineering, mechanics, software and arts.
Control of robots relies on many areas of robotics, including:[3]
• Adaptive control – control method used by a controller which must adapt to a controlled system with parameters
which vary, or are initially uncertain. For example, as an aircraft flies, its mass will slowly decrease as a result of
fuel consumption; a control law is needed that adapts itself to such changing conditions.
• Aerial robotics –
• Anthrobotics – science of developing and studying robots that are either entirely or in some way human-like.
• Artificial intelligence – the intelligence of machines and the branch of computer science that aims to create it.
• Autonomous car – an autonomous vehicle capable of fulfilling the human transportation capabilities of a
traditional car
• Autonomous research robotics –
• Bayesian network –
• BEAM robotics – a style of robotics that primarily uses simple analogue circuits instead of a microprocessor in
order to produce an unusually simple design (in comparison to traditional mobile robots) that trades flexibility for
robustness and efficiency in performing the task for which it was designed.
• Behavior-based robotics – the branch of robotics that incorporates modular or behavior based AI (BBAI).
• Biomimetic – see Bionics.
• Biomorphic robotics – a sub-discipline of robotics focused upon emulating the mechanics, sensor systems,
computing structures and methodologies used by animals.
• Bionics – also known as biomimetics, biognosis, biomimicry, or bionical creativity engineering is the application
of biological methods and systems found in nature to the study and design of engineering systems and modern
technology.
• Biorobotics – a study of how to make robots that emulate or simulate living biological organisms mechanically or
even chemically.
• Cognitive robotics – views animal cognition as a starting point for the development of robotic information
processing, as opposed to more traditional Artificial Intelligence techniques.
• Clustering –
• Computational neuroscience – study of brain function in terms of the information processing properties of the
structures that make up the nervous system.
• Robot control – a study of controlling robots
• Robotics conventions –
• Data mining Techniques –
• Degrees of freedom – in mechanics, the degree of freedom (DOF) of a mechanical system is the number of
independent parameters that define its configuration. It is the number of parameters that determine the state of a
physical system and is important to the analysis of systems of bodies in mechanical engineering, aeronautical
engineering, robotics, and structural engineering.
• Developmental Robotics – a methodology that uses metaphors from neural development and developmental
psychology to develop the mind for autonomous robots
• Digital control – a branch of control theory that uses digital computers to act as system controllers.
86
Outline of robotics
• Digital image processing – the use of computer algorithms to perform image processing on digital images.
• Dimensionality reduction – the process of reducing the number of random variables under consideration, and can
be divided into feature selection and feature extraction.
• Distributed robotics –
• Electronic Stability Control – is a computerized technology that improves the safety of a vehicle's stability by
detecting and reducing loss of traction (skidding).
• Evolutionary computation –
• Evolutionary robotics – a methodology that uses evolutionary computation to develop controllers for autonomous
robots
• Extended Kalman filter –
• Flexible Distribution functions –
• Feedback control and Regulation –
• Human–computer interaction – a study, planning and design of the interaction between people (users) and
computers
• Human robot interaction – a study of interactions between humans and robots
• Kinematics – study of motion, as applied to robots. This includes both the design of linkages to perform motion,
their power, control and stability; also their planning, such as choosing a sequence of movements to achieve a
broader task.
• Laboratory robotics – the act of using robots in biology or chemistry labs
• Robot learning – learning to perform tasks such as obstacle avoidance, control and various other motion-related
tasks
• Manifold learning –
• Direct manipulation interface – In computer science, direct manipulation is a human–computer interaction style
which involves continuous representation of objects of interest and rapid, reversible, and incremental actions and
feedback. The intention is to allow a user to directly manipulate objects presented to them, using actions that
correspond at least loosely to the physical world.
• Robotic mapping – the goal for an autonomous robot to be able to construct (or use ) a map or floor plan and to
localize itself in it
• Microrobotics – a field of miniature robotics, in particular mobile robots with characteristic dimensions less than
1 mm
• Motion planning – (a.k.a., the "navigation problem", the "piano mover's problem") is a term used in robotics for
the process of detailing a task into discrete motions.
• Motor control – information processing related activities carried out by the central nervous system that organize
the musculoskeletal system to create coordinated movements and skilled actions.
• Nanorobotics – the emerging technology field creating machines or robots whose components are at or close to
the scale of a nanometer (10−9 meters).
• Artificial neural networks – a mathematical model inspired by biological neural networks.
• Passive dynamics – refers to the dynamical behavior of actuators, robots, or organisms when not drawing energy
from a supply (e.g., batteries, fuel, ATP).
• Reinforcement learning – an area of machine learning in computer science, concerned with how an agent ought to
take actions in an environment so as to maximize some notion of cumulative reward.
• Programming by Demonstration – an End-user development technique for teaching a computer or a robot new
behaviors by demonstrating the task to transfer directly instead of programming it through machine commands.
• Robot kinematics – applies geometry to the study of the movement of multi-degree of freedom kinematic chains
that form the structure of robotic systems.
• Robot locomotion – collective name for the various methods that robots use to transport themselves from place to
place.
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Outline of robotics
• Rapid prototyping – automatic construction of physical objects via additive manufacturing from virtual models in
computer aided design (CAD) software, transforming them into thin, virtual, horizontal cross-sections and then
producing successive layers until the items are complete. As of June 2011, used for making models, prototype
parts, and production-quality parts in relatively small numbers.
• Robot programming –
• Sensors – (also called detector) is a converter that measures a physical quantity and converts it into a signal which
can be read by an observer or by an (today mostly electronic) instrument.
• Simultaneous localization and mapping – a technique used by robots and autonomous vehicles to build up a map
within an unknown environment (without a priori knowledge), or to update a map within a known environment
(with a priori knowledge from a given map), while at the same time keeping track of their current location.
• Software engineering – the application of a systematic, disciplined, quantifiable approach to the design,
development, operation, and maintenance of software, and the study of these approaches; that is, the application
of engineering to software.
• Speech processing – study of speech signals and the processing methods of these signals. The signals are usually
processed in a digital representation, so speech processing can be regarded as a special case of digital signal
processing, applied to speech signal.[clarification needed] Aspects of speech processing includes the acquisition,
manipulation, storage, transfer and output of digital speech signals.
• Support vector machines – supervised learning models with associated learning algorithms that analyze data and
recognize patterns, used for classification and regression analysis.
• Robotic surgery – computer-assisted surgery, and robotically-assisted surgery are terms for technological
developments that use robotic systems to aid in surgical procedures.
• Remote surgery – (also known as telesurgery) is the ability for a doctor to perform surgery on a patient even
though they are not physically in the same location.
• Robot-assisted heart surgery –
• Swarm robotics – involves large numbers of mostly simple physical robots. Their actions may seek to incorporate
emergent behavior observed in social insects (swarm intelligence).
• Ant robotics – swarm robots that can communicate via markings, similar to ants that lay and follow
pheromone trails.
• Telepresence – refers to a set of technologies which allow a person to feel as if they were present, to give the
appearance of being present, or to have an effect, via telerobotics, at a place other than their true location.
• Intelligent vehicle technologies – comprise electronic, electromechanical, and electromagnetic devices - usually
silicon micromachined components operating in conjunction with computer controlled devices and radio
transceivers to provide precision repeatability functions (such as in robotics artificial intelligence systems)
emergency warning validation performance reconstruction.
• Computer vision –
• Machine vision –
88
Outline of robotics
Contributing fields
• Aerospace –
• Biology –
• Biomechanics –
• Computer science –
• Artificial Intelligence –
• Computational linguistics –
• Cybernetics –
• Modal logic –
• Engineering –
•
•
•
•
•
•
Acoustical engineering –
Automotive engineering –
Chemical engineering –
Control engineering –
Electrical engineering –
Electronic engineering –
• Mechanical engineering –
• Mechatronics engineering –
• Microelectromechanical engineering –
• Nanoengineering –
• Optical engineering –
• Safety engineering –
• Software engineering –
• Telecommunications –
• Fiction – Robotics technology and its implications are major themes in science fiction and have provided
inspiration for robotics development and cause for ethical concerns. Robots are portrayed in short stories and
novels, in movies, in TV shows, in theatrical productions, in web based media, in computer games, and in comic
books. See List of fictional robots and androids.
• Film – See Robots in film.
• Literature – fictional autonomous artificial servants have a long history in human culture. Today's most
pervasive trope of robots, developing self-awareness and rebelling against their creators, dates only from the
early 20th century. See Robots in literature.
• The Three Laws of Robotics in popular culture
• Military science –
• Psychology –
• Cognitive science –
• Behavioral science –
• Philosophy –
• Ethics –
• Physics –
• Dynamics –
• Kinematics –
Additionally, contributing fields include the specific field(s) a particular robot is being designed for. Expertise in
surgical procedures and anatomy, for instance would be required for designing robotic surgery applications.
89
Outline of robotics
Related fields
• Building automation –
• Home automation –
Robots
Types of robots
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Aerobot – robot capable of independent flight on other planets.
Android – humanoid robot. Robot resembling the shape or form of a human.
Automaton – early self-operating robot, performing exactly the same actions, over and over.
Autonomous vehicle – vehicle equipped with an autopilot system, which is capable of driving from one point to
another without input from a human operator.
• Ballbot – dynamically-stable mobile robot designed to balance on a single spherical wheel (i.e., a ball).
• Cruise missile – robot-controlled guided missile that carries an explosive payload.
• Cyborg – also known as a cybernetic organism, a being with both biological and artificial (e.g. electronic,
mechanical or robotic) parts.
• Explosive ordnance disposal robot – mobile robot designed to assess whether an object contains explosives; some
carry detonators that can be deposited at the object and activated after the robot withdraws.
• Gynoid – humanoid robot designed to look like a human female.
• Hexapod (walker) – A six-legged walking robot, using a simple insect-like locomotion.
• Industrial robot – reprogrammable, multifunctional manipulator designed to move material, parts, tools, or
specialized devices through variable programmed motions for the performance of a variety of tasks.
• Insect robot – small robot designed to imitate insect behaviors rather than complex human behaviors.
• Mobile robot – self-propelled and self-contained robot that is capable of moving over a mechanically
unconstrained course.
• Prosthetic robot – programmable manipulator or device replacing a missing human limb.
• Service robot – machines that extend human capabilities.
• Snakebot – robot or robotic component resembling a tentacle or elephant's trunk, where many small actuators are
used to allow continuous curved motion of a robot component, with many degrees of freedom. This is usually
applied to snake-arm robots, which use this as a flexible manipulator. A rarer application is the snakebot, where
the entire robot is mobile and snake-like, so as to gain access through narrow spaces.
• Surgical robot – remote manipulator used for keyhole surgery
• Walking robot – robot capable of locomotion by walking. Owing to the difficulties of balance, two-legged
walking robots have so far been rare and most walking robots have used insect-like multilegged walking gaits.
• microbot- microscopic robots designed to go into the human body and cure diseases.
• nanobot -same as a microbot, just smaller.
• rover (space exploration) -a robot with wheels designed to walk on other planets floors.
• autonomous robot- robots that are not controlled by humans.
90
Outline of robotics
By mode of locomotion
Mobile robots may be classified by:
• The environment in which they travel:
• Land or home robots. They are most commonly wheeled, but also include legged robots with two or more legs
(humanoid, or resembling animals or insects).
• Aerial robots are usually referred to as unmanned aerial vehicles (UAVs)
• Underwater robots are usually called autonomous underwater vehicles (AUVs)
• Polar robots, designed to navigate icy, crevasse filled environments
• The device they use to move, mainly:
• Legged robot : human-like legs (i.e. an android) or animal-like legs.
• Wheeled robot.
• Tracks.[4]
Robot components and design features
• Actuator – motor that translates control signals into mechanical movement. The control signals are usually
electrical but may, more rarely, be pneumatic or hydraulic. The power supply may likewise be any of these. It is
common for electrical control to be used to modulate a high-power pneumatic or hydraulic motor.
• Linear actuator – form of motor that generates a linear movement directly.
• Delta robot – tripod linkage, used to construct fast-acting manipulators with a wide range of movement.
• Drive Power – energy source or sources for the robot actuators.
• End-effector – accessory device or tool specifically designed for attachment to the robot wrist or tool mounting
plate to enable the robot to perform its intended task. (Examples may include gripper, spot-weld gun, arc-weld
gun, spray- paint gun, or any other application tools.)
• Forward chaining – process in which events or received data are considered by an entity to intelligently adapt its
behavior.
• Haptic – tactile feedback technology using the operator's sense of touch. Also sometimes applied to robot
manipulators with their own touch sensitivity.
• Hexapod (platform) – movable platform using six linear actuators. Often used in flight simulators and fairground
rides, they also have applications as a robotic manipulator.
See Stewart platform
• Hydraulics – control of mechanical force and movement, generated by the application of liquid under pressure.
c.f. pneumatics.
• Kalman filter – mathematical technique to estimate the value of a sensor measurement, from a series of
intermittent and noisy values.
• Klann linkage – simple linkage for walking robots.
• Manipulator – gripper. A robotic 'hand'.
• Parallel manipulator – articulated robot or manipulator based on a number of kinematic chains, actuators and
joints, in parallel. c.f. serial manipulator.
• Remote manipulator – manipulator under direct human control, often used for work with hazardous materials.
• Serial manipulator – articulated robot or manipulator with a single series kinematic chain of actuators. c.f.
parallel manipulator.
• Muting – deactivation of a presence-sensing safeguarding device during a portion of the robot cycle.
• Pendant – Any portable control device that permits an operator to control the robot from within the restricted
envelope (space) of the robot.
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Outline of robotics
• Pneumatics – control of mechanical force and movement, generated by the application of compressed gas. c.f.
hydraulics.
• Servo – motor that moves to and maintains a set position under command, rather than continuously moving.
• Servomechanism – automatic device that uses error-sensing negative feedback to correct the performance of a
mechanism.
• Single Point of Control – ability to operate the robot such that initiation or robot motion from one source of
control is possible only from that source and cannot be overridden from another source.
• Slow Speed Control – mode of robot motion control where the velocity of the robot is limited to allow persons
sufficient time either to withdraw the hazardous motion or stop the robot.
• Stepper motor –
• Stewart platform – movable platform using six linear actuators, hence also known as a Hexapod.
• Subsumption architecture – robot architecture that uses a modular, bottom-up design beginning with the least
complex behavioral tasks.
• Teach Mode – control state that allows the generation and storage of positional data points effected by moving
the robot arm through a path of intended motions.
Specific robots
• Aura (satellite) – robotic spacecraft launched by NASA in 2004 which collects atmospheric data from Earth.
• Chandra X-ray Observatory – robotic spacecraft launched by NASA in 1999 to collect astronomical data.
• Robonaut – development project conducted by NASA to create humanoid robots capable of using space tools and
working in similar environments to suited astronauts.
• Unimate – the first off-the-shelf industrial robot, of 1961.
Real robots by region
Robots from Australia
• GuRoo –
• UWA Telerobot –
Robots from Britain
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Black Knight
eSTAR
Freddy II
George
Shadow Hand
Silver Swan
Talisman UUV
Wheelbarrow
Robop
92
Outline of robotics
Robots from Canada
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Canadarm2 –
Dextre –
ANATROLLER ARI-100 –
ANATROLLER ARE-100 –
ANATROLLER ARI-50 –
ANATROLLER Dusty Duct Destroyer –
ANAT AMI-100 –
Robots from China
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FemiSapien –
Meinü robot –
RoboSapien –
Robosapien v2 –
RS Media –
Xianxingzhe –
Robots from Croatia
• DOK-ING EOD –
• TIOSS –
Robots from Czech Republic
• SyRoTek –
Robots from France
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Digesting Duck –
Jessiko –
Nabaztag –
Nao –
Robots from Germany
• Marvin –
• Care-Providing Robot FRIEND –
• LAURON –
Robots from Italy
• IsaacRobot –
• Leonardo's robot –
93
Outline of robotics
Robots from Japan
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AIBO –
ASIMO –
Choromet –
EMIEW –
EMIEW 2 –
Enon –
Evolta –
Gakutensoku –
HAL 5v
HOAP –
KHR-1 –
Omnibot –
Plen –
QRIO –
R.O.B. –
SCARA –
• Toyota Partner Robot –
• Wakamaru –
Robots from Mexico
• Don Cuco El Guapo –
Robots from the Netherlands
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Adelbrecht –
Flame –
Phobot –
Senster –
Robots from New Zealand
• Trons, The –
Robots from Portugal
• RAPOSA –
Robots from Qatar
• Robot jockey –
Robots from Russia (or former Soviet Union)
• Lunokhod 1 –
• Lunokhod 2 –
• Teletank –
94
Outline of robotics
Robots from South Korea
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Albert Hubo –
EveR-1 –
HUBO –
MAHRU –
Musa –
Robots from Spain
• Maggie –
• REEM-B –
• Tico –
Robots from Switzerland
• Alice mobile robot –
• E-puck mobile robot –
• Pocketdelta robot –
Robots from the United States of America
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Albert One –
Allen –
ATHLETE –
Ballbot –
avbotz Baracuda XIV –
Beer Launching Fridge –
Berkeley Lower Extremity Exoskeleton –
BigDog –
Boe-Bot –
Coco –
Cog –
Crusher –
Dragon Runner –
EATR –
Elektro –
Entomopter –
Haile –
Hardiman –
HERO –
Johns Hopkins Beast –
Kismet –
Leonardo –
LOPES –
LORAX –
Nomad 200 –
Nomad rover –
Opportunity rover –
• Programmable Universal Machine for Assembly –
• Push the Talking Trash Can –
95
Outline of robotics
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RB5X –
Robonaut –
Shakey the Robot –
Sojourner –
Spirit rover –
Turtle –
Unimate –
Zoë –
Pleo –
Robots from Vietnam
• TOPIO –
International robots
• European Robotic Arm –
• Curiosity Rover for NASA on Mars Science Laboratory space mission –
Fictional robots by region
Fictional robots from the United Kingdom
From British literature
• HAL 9000 (Arthur C. Clarke) –
From British radio
• Marvin the Paranoid Android (Douglas Adams) –
From British television
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Kryten (Rob Grant, Doug Naylor, David Ross, Robert Llewellyn) {Red Dwarf} –
Talkie Toaster – (Rob Grant, Doug Naylor, John Lenahan, David Ross) {Red Dwarf}
K-9 (Doctor Who) –
Robotboy – (Bob Camp, Charlie Bean, Heath Kenny, Prof Moshimo, Laurence Bouvard) {Robotboy}
Fictional robots from the Czech Republich
From Czech plays
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Daemon – (Karel Čapek) {R.U.R. (Rossum's Universal Robots)}
Helena – (Karel Čapek) {R.U.R. (Rossum's Universal Robots)}
Marius – (Karel Čapek) {R.U.R. (Rossum's Universal Robots)}
Primus – (Karel Čapek) {R.U.R. (Rossum's Universal Robots)}
Radius – (Karel Čapek) {R.U.R. (Rossum's Universal Robots)}
Sulla – (Karel Čapek) {R.U.R. (Rossum's Universal Robots)}
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Outline of robotics
Fictional robots from France
From French ballets
• Coppélia – (Arthur Saint-Leon, Léo Delibes) {Coppélia}
From French literature
• Hadaly – (Auguste Villiers de l'Isle-Adam) {The Future Eve}
Fictional robots from Germany
From German film
• Maschinenmensch – (Fritz Lang, Thea von Harbou, Brigitte Helm) {Metropolis}
From German literature
• Maschinenmensch – (Thea von Harbou)
• Olimpia – (E. T. A. Hoffmann) {Der Sandmann}
Fictional robots from Japan
From anime
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Braiger – (Shigeo Tsubota, Tokichi Aoki) {Ginga Senpuu Braiger}
Combattler V – (Tadao Nagahama, Saburo Yatsude) {Super Electromagnetic Robo Combattler V}
Daimos – (Tadao Nagahama, Saburo Yatsude) {Brave Leader Daimos}
Groizer X – (Go Nagai) {Groizer X}
Mechander Robo – (Jaruhiko Kaido) {Mechander Robo (Gasshin Sentai Mekandaa Robo)}
Raideen – (Yoshiyuki Tomino, Tadao Nagahama) {Brave Raideen}
Trider G7 – (Hajime Yatate) {Invincible Robo Trider G7}
Voltes V – (Tadao Nagahama, Saburo Yatsude) {Super Electromagnetic Machine Voltes V}
From manga
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Astro Boy – (Osamu Tezuka) {Astro Boy}
Doraemon – (Fujiko Fujio) {Doraemon}
Getter Robo – (Go Nagai, Ken Ishikawa) {Getter Robo}
Grendizer – (Go Nagai) {UFO Robo Grendizer}
Mazinger Z – (Go Nagai) {Mazinger Z}
Tetsujin 28 – (Mitsuteru Yokoyama) {Tetsujin 28 - Go!}
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Outline of robotics
Fictional robots from the United States of America
From American comics
• Amazo – (Gardner Fox) {DC Comics}
• Annihilants – (Alex Raymond) {Flash Gordon}
From American film
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C-3PO – (George Lucas, Anthony Daniels) {Star Wars}
ED-209 – (Paul Verhoeven, Craig Hayes, Phil Tippett) {RoboCop}
Gort – (Robert Wise, Harry Bates, Edmund H. North, Lock Martin) {The Day the Earth Stood Still}
R2-D2 – (George Lucas, Kenny Baker, Ben Burtt) {Star Wars}
Robby the Robot – (Fred M. Wilcox, Robert Kinoshita, Frankie Darro, Marvin Miller) {Forbidden Planet}
The Terminator – (James Cameron, Gale Anne Hurd) {The Terminator}
From American literature
• Adam Link – (Eando Binder) {I, Robot}
• Gnut – (Harry Bates) {Farewell to the Master}
• Robbie – (Isaac Asimov) {I, Robot}
• The Steam Man of the Prairies – (Edward S. Ellis) {The Steam Man of the Prairies}
• Tik-Tok – (L. Frank Baum) {Ozma of Oz}
From American television
• Bender Bending Rodriguez – (Matt Groening, David X. Cohen, John DiMaggio) {Futurama}
• Cambot – Gypsy, Crow T. Robot, and Tom Servo (Joel Hodgson, Trace Beaulieu, Bill Corbett, Josh Weinstein,
Jim Mallon, Patrick Brantseg) {Mystery Science Theater 3000}
• Data – (Gene Roddenberry, Brent Spiner) {Star Trek: The Next Generation}
• Jenny Wakeman – (Rob Rezenti, Janice Kawaye) {My Life as a Teenage Robot}
• Robot B-9 – (Irwin Allen, Robert Kinoshita, Bob May, Dick Tufeld) {Lost in Space}
• Grounder and Scratch – (Phil Hayes, Gary Chalk ) {Adventures of Sonic the Hedgehog}
Robotics development and development tools
• Arduino – current platform of choice for small-scale robotic experimentation and physical computing.
• CAD/CAM (computer-aided design and computer-aided manufacturing) – these systems and their data may be
integrated into robotic operations.
• Cleanroom – environment that has a low level of environmental pollutants such as dust, airborne microbes,
aerosol particles and chemical vapors; often used in robot assembly.
Robotics principles
• Artificial intelligence – intelligence of machines and the branch of computer science that aims to create it.
• Degrees of freedom – extent to which a robot can move itself; expressed in terms of Cartesian coordinates (x, y,
and z) and angular movements (yaw, pitch, and roll).
• Emergent behaviour – complicated resultant behaviour that emerges from the repeated operation of simple
underlying behaviours.
• Envelope (Space), Maximum – volume of space encompassing the maximum designed movements of all robot
parts including the end-effector, workpiece, and attachments.
• Humanoid – resembling a human being in form, function, or both.
98
Outline of robotics
• Three Laws of Robotics – coined by the science fiction author Isaac Asimov, one of the first serious
considerations of the ethics and robopsychological aspects of robotics.
• Tool Center Point (TCP) – origin of the tool coordinate system.
• Uncanny valley – hypothesized point at which humanoid robot behavior and appearance is so close to that of
actual humans yet not precise or fully featured enough as to cause a sense of revulsion.
Applications of robotics
• Combat, robot – hobby or sport event where two or more robots fight in an arena to disable each other. This has
developed from a hobby in the 1990s to several TV series worldwide.
Robotics organizations
• FIRST (For Inspiration and Recognition of Science and Technology) – organization founded by inventor Dean
Kamen in 1989 in order to develop ways to inspire students in engineering and technology fields. It founded
various robotics competitions for elementary and high school students.
Robotics competitions
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National ElectroniX Olympiad
ABU Robocon
BEST Robotics
Botball
DARPA Grand Challenge – prize competition for American autonomous vehicles, funded by the Defense
Advanced Research Projects Agency, the most prominent research organization of the United States Department
of Defense.
• DARPA Grand Challenge (2004)
• DARPA Grand Challenge (2005)
• DARPA Grand Challenge (2007)
DARPA Robotics Challenge
Defcon Robot Contest
Duke Annual Robo-Climb Competition
Eurobot
European Land-Robot Trial
FIRST Junior Lego League
FIRST Lego League
FIRST Robotics Competition
FIRST Tech Challenge
International Aerial Robotics Competition
Micromouse
National Engineering Robotics Contest
RoboCup
Robofest
RoboGames
RoboSub
Student Robotics
• UAV Outback Challenge
• World Robot Olympiad
99
Outline of robotics
People influential in the field of robotics
• Asimov, Isaac – science fiction author who coined the term "robotics", and wrote the three laws of robotics.
• Čapek, Karel – Czech author who coined the term "robot" in his 1921 play, Rossum's Universal Robots.
Robotics in popular culture
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Droid
List of fictional cyborgs
List of fictional robots and androids
List of fictional gynoids and female cyborgs
Real Robot
Super Robot
Robot Hall of Fame
Waldo – a short story by Robert Heinlein, that gave its name to a popular nickname for remote manipulators.
References
[1] http:/ / en. wikipedia. org/ w/ index. php?title=Template:TopicTOC-Robotics& action=edit
[2] According to the Oxford English Dictionary, the term "robotics" was first used in the short story "Liar!" published in the May, 1941 issue of
Astounding Science Fiction.
[3] http:/ / robots. newcastle. edu. au/
[4] Rail track (http:/ / prweb. com/ releases/ Rail/ Robot/ prweb453019. htm) and Linear track (PDF) (http:/ / www. labautomationrobots. com/
images/ crstrack. pdf)
External links
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Robotics (http://www.dmoz.org/Computers/Robotics/) on the Open Directory Project
Autonomous Programmable Robot (http://www.moway-robot.com/index.php?lang=en)
Four-leg robot (http://www.youtube.com/watch?v=egpBRjFqNWA)
Robotics Resources at CMU (http://www.cs.cmu.edu/~chuck/robotpg/robo_rsrc.html)
Society of Robots (http://www.societyofrobots.com/)
Research
• The evolution of robotics research (http://ieeexplore.ieee.org/search/srchabstract.jsp?arnumber=4141037&
isnumber=4141014&punumber=100&k2dockey=4141037@ieeejrns&query=((robotics)<in>metadata)&
pos=0&access=no)
• Human Machine Integration Laboratory (http://robotics.eas.asu.edu/) at Arizona State University
• International Foundation of Robotics Research (IFRR) (http://www.ifrr.org)
• International Journal of Robotics Research (IJRR) (http://www.ijrr.org)
• Robotics and Automation Society (RAS) (http://www.ieee-ras.org) at IEEE
• Robotics Network (http://kn.theiet.org/communities/robotics/index.cfm) at IET
• Robotics Division (http://robotics.nasa.gov) at NASA
• Robotics and Intelligent Machines at Georgia Tech (http://robotics.gatech.edu/)
• Robotics Institute at Carnegie Mellon (http://www.ri.cmu.edu/)
100
Article Sources and Contributors
Article Sources and Contributors
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Aceituno, Acohen843, AdjustShift, Ahoerstemeier, Aillema, Airogos, Aitias, Ajayvarma mav, Al.Glitch, Alansohn, Ale jrb, Allens, Aloisdimpflmoser, Alperkaradas, Andreas-B, Andres,
Andrevan, Andrew Maiman, Andy Dingley, AniketxD, Ankuredkie, Anonymous Dissident, Antipastor, Antonio Lopez, Aranea Mortem, ArchonMagnus, Arctic Kangaroo, Arjen IF, Arthena,
Ashermehul123, Asifparacha786, Asthme, Auntof6, Austin512, Avicennasis, Avnjay, Ayrton Prost, BAxelrod, BD2412, Backslash Forwardslash, Bacteria, Bakic, Bandaruvamsi, Bdenneen,
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Actuator Source: https://en.wikipedia.org/w/index.php?oldid=581715726 Contributors: A876, Accotink2, Alexjwall91, Allens, Altermike, Amatulic, Andres, Andy Dingley, Andymarczak,
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Shootbamboo, Static poof, 14 anonymous edits
Computer vision Source: https://en.wikipedia.org/w/index.php?oldid=583700035 Contributors: 155.54.12.xxx, 66.24.100.xxx, APH, APerson, Aaronh, Ademarius, Ahy1, Albert Chen, Alksub,
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Mobile manipulator Source: https://en.wikipedia.org/w/index.php?oldid=578440727 Contributors: Büropalme, Chaosdruid, Danim, Ego White Tray, Hvilshoj, Katharineamy, MrRadioGuy,
MuffledThud, Robocero, Sboegh, 13 anonymous edits
Robot locomotion Source: https://en.wikipedia.org/w/index.php?oldid=559648433 Contributors: Alynna Kasmira, BD2412, Blue520, Chaosdruid, Danim, Drawingshadows, EagleFan, HCA,
Hasanp, JQF, Jrockley, Melaen, Michael Snow, Nagle, Pawyilee, Rich Farmbrough, RockMagnetist, Ronz, RussBlau, SpaceFlight89, TS133, Takeitupalevel, Thorenn, Wavelength, 24
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Mobile robot Source: https://en.wikipedia.org/w/index.php?oldid=581429862 Contributors: Aboeing, Altermike, Androidchild, Antonbharkamsan, Appraiser, Bakabaka, Bhadani, Blasian44,
Calltech, Catskul, Chaosdruid, Chojitsa, Danim, Dawdler, Debresser, Dendik, Fartherred, Favonian, Feneer, Firsfron, Fraggle81, Gh5046, Grantmidnight, Gurch, Hogyn Lleol, Hu, Hvilshoj,
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Robotic mapping Source: https://en.wikipedia.org/w/index.php?oldid=585216266 Contributors: "alyosha", Aboeing, Ademkader, Altermike, Angela, Blitzvergnugen, Bwmodular, Cindamuse,
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Njm7203, Pakaraki, Rjn, Ronz, Rror, Sabry hassouna, Smartyhall, Twocs, Wickerzone, 32 anonymous edits
Human–robot interaction Source: https://en.wikipedia.org/w/index.php?oldid=587566248 Contributors: Aaronpowers, Alanschultz, Arjayay, Babajobu, Barattini, Bennievermeer, Candrlic,
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Artificial intelligence Source: https://en.wikipedia.org/w/index.php?oldid=587948546 Contributors: 100110100, 132.204.25.xxx, 172.141.188.xxx, 17Drew, 1dragon, 1exec1, 200.191.188.xxx,
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Image Sources, Licenses and Contributors
Image Sources, Licenses and Contributors
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Greenhill and Hugo Elias (myself) of the Shadow Robot Company
File:2005-11-14 ShadowLeg Finished medium.jpg Source: https://en.wikipedia.org/w/index.php?title=File:2005-11-14_ShadowLeg_Finished_medium.jpg License: Creative Commons
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File:Automation of foundry with robot.jpg Source: https://en.wikipedia.org/w/index.php?title=File:Automation_of_foundry_with_robot.jpg License: Public Domain Contributors: KUKA
Roboter GmbH, Bachmann
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Haplochromis, Hike395
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EugeneZelenko, Mikael Häggström, TommyBee, WikipediaMaster, 2 anonymous edits
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