Download The final step of designing and implementing a reactive system is to

Notes by Santosh Sir... 9867211982/8976381939...AARYA CLASSES...www.aaryaclasses.co.in
Q. Steps in Designing a Reactive Behavioral System
Step 1: Describe the task. The purpose of this step is to specify what the robot has to do to be
successful. The task was for the robot vehicle to follow a path with hair pin turns, stationary
obstacles in the path, and a sand pit. The robot which went the furthest without going completely
out of bounds was the winner, unless two or more robots went the same distance or completed
the course, then the winner was whoever went the fastest.
Step 2: Describe the robot. The purpose of this step is to determine the basic physical abilities
of the robot and any limitations. In theory, it might be expected that the designer would have
control over the robot itself, what it could do, what sensors it carries, etc. The designer is usually
handed some fixed constraints on the robot platform which will impact the design.
Step 3: Describe the Environment. This step is critical for two reasons. First, it is a key factor
in determining the situatedness of the robot. Second, it identifies perceptual opportunities for the
behaviors, both in how a perceptual event will instantiate a new behavior, and in how the
perceptual schema for a behavior will function.
Step 4: Describe how the robot should act in response to its environment.
The purpose of this step is to identify the set of one or more candidate primitive behaviors; these
candidates will be refined or eliminated later. As the designer describes how the robot should act,
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behaviors usually become apparent. It should be emphasized that the point of this step is to
concentrate on what the robot should do, not how it will do it, although often the designer sees
both the what and the how at the same time. In terms of expressing the behaviors for a task, it is
often advantageous to construct a behavior table as one way of BEHAVIOR TABLE at least
getting all the behaviors on a single sheet of paper.
Step 5: Refine each behavior. By this point, the designer has an overall idea of the organization
of the reactive system and what the activities are. This step concentrates on the design of each
individual behavior. As the designer constructs the underlying algorithms for the motor and
perceptual schemas, it is important to be sure to consider both the normal range of environmental
conditions the robot is expected to operate in and when the behavior will fail.
Step 6: Test each behavior independently.
As in any software engineering project, modules or behaviors are tested individually. Ideally,
testing occurs in simulation prior to testing on the robot in its environment. Many commercially
available robots such as Khepera and Nomads come with impressive simulators. However, it is
important to remember that simulators often only model the mechanics of the robot, not the
perceptual abilities.
Step 7: Test with other behaviors.
The final step of designing and implementing a reactive system is to perform integration testing,
where the behaviors are combined. This also includes testing the behaviors in the actual
environment.
Q. Attributes of a sensor
1. Field of view and range. Every exteroceptive sensor has a region of space that it is intended
to cover. The width of that region are specified by the sensor’s field of view, often abbreviated as
FOV. The field of view is usually expressed in degrees; the number of degrees covered vertically
may be different from the number of degrees covered horizontally. Field of view is frequently
used in photography, where different lenses capture different size and shape areas. A wide angle
lens will often cover up to 70%, while a “regular” lens may only have a field of view around
27%. The distance that the field extends is called the range.
2. Accuracy, repeatability, and resolution. Accuracy refers to how correct the reading from the
sensor is. But if a reading for the same conditions is accurate only 20% of the time, then the
sensor has little repeatability. If the sensor is consistently inaccurate in the same way (always 2
or 3 cm low), then the software can apply a bias (add 2 centimeters) to compensate. If the
inaccuracy is random, then it will be difficult to model and the applications where such a sensor
can be used will be limited.
3. Responsiveness in the target domain. Most sensors have particular environments in which
they function poorly. Another way of viewing this is that the environment must allow the signal
of interest to be extracted from noise and interference (e.g., have a favorable signal-to-noise
ratio). As will be seen below, sonar is often unusable for navigating in an office foyer with
large amounts of glass because the glass reflects the sound energy in ways almost impossible to
predict. It is important to have characterized the ecological niche of the robot in terms of what
will provide, absorb, or deflect energy.
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Notes by Santosh Sir... 9867211982/8976381939...AARYA CLASSES...www.aaryaclasses.co.in
4. Power consumption. Power consumption is always a concern for robots. Since most robots
operate off of batteries, the less power they consume, the longer they run. For example, the
battery life on a Nomad 200, which carries five batteries was improved from four hours to six by
shutting off all sensors. Power is so restricted on most mobile robots that many robot
manufacturers will swap microprocessor chips just to reduce the power drain
5. Hardware reliability. Sensors often have physical limitations on how well they work. For
example, Polaroid sonars will produce incorrect range reading when the voltage drops below
12V. Other sensors have temperature and moisture constraints which must be considered.
6. Size. The size and weight of a sensor does affect the overall design. A microrover on the order
of a shoebox will not have the power to transport a large camera or camcorder, but it may be able
to use a miniature “Quick-Cam” type of camera.
8. Interpretation reliability. The designer should consider how reliable the sensor will be for
the ecological conditions and for interpretation. The robot will often have no way of determining
when a sensor is providing incorrect information.
Q. GPS
GPS, or Global Positioning System, is becoming more common on robots, especially those used
to automate farm equipment (an effort called precision agriculture). GPS systems work by
receiving signals from satellites orbiting the Earth.
The receiver triangulates itself relative to four GPS satellites, computing its position in terms of
latitude, longitude, altitude, and change in time. GPS isn’t a proprioceptive sensor per se since
the robot must receive signals from the satellites, external to the robot. However, they are not
exteroceptive sensors either, since the robot isn’t computing its position relative to its
environment.
Currently the only sets of GPS satellites that a receiver can triangulate itself against are the
Navstar “constellation” maintained by the United States Air Force Space Command or the
Russian counterpart, GLONOSS, maintained by the Russian Federation Ministry of Defense.
There are two types of channels on Navstar, one public, called the Standard Positioning System,
and an encrypted signal, the Precise Positioning System.
Until early in the year 2000, the U.S. military actually introduced an error in the satellite
message as to where the satellite actually is, which could result in triangulation errors of up to
100meters. Selective availability was turned off in part because of the rise of civilian uses of
GPS, and because it led to interoperability with groups working with the U.S. military who were
using commercial, not military, GPS.
Many inexpensive hand-held receivers sold to hunters and hikers attempt to improve on
localization by averaging or filtering the readings. This can reduce the error down to 10-15
meters.
The method is called differential GPS (DGPS), where two GPS receivers are used. One remains
stationary, while the other is put on the robot.
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If the two receivers are observing the same satellites, then any sudden change in position on the
stationary “base” receiver is due to the induced error and can be subtracted from the readings at
the robot GPS.
GPS and DGPS are not complete solutions to the dead reckoning problem in mobile robots for at
least two reasons. First, GPS does not work indoors in most buildings, especially offices or
factories with large amounts of steel reinforced concrete. As with cellular phones, these
structures interrupt the reception of radio signals.
Likewise, GPS may not work outdoors in major cities where skyscrapers act as urban canyons
and interfere with reception. Second, commercial DGPS systems cost on the order of $30,000
USD, which is prohibitively high. Several web sites now offer free “do-it-yourself” DGPS code
to create a DGPS from two inexpensive receivers.
Q. Proximity Sensors
Proximity sensors measure the relative distance (range) between the sensor and objects in the
environment. Since the sensor is mounted on the robot, it is a straightforward computation to
translate a range relative to the sensor to a range relative to the robot at large. Most proximity
sensors are active. Sonar, also called ultrasonics, is the most popular proximity sensor, with
infrared, bump, and feeler sensors
Sonar or ultrasonics
Sonar refers to any system for using sound to measure range. Sonars for different applications
operate at different frequencies; for example, a sonar for underwater vehicles would use a
frequency appropriate for traveling through water, while a ground vehicle would use a frequency
more suited for air. Ground vehicles commonly use sonars with an ultrasonic frequency,
just at the edge of human hearing. As a result the terms “sonar” and “ultrasonics” are used
interchangeably when discussing extracting range from acoustic energy.
Ultrasonics is common for several reasons. Its evolution paralleled the rise of the Reactive
Paradigm. In the mid-1980’s, Hans Moravec did impressive robot navigation with a ring of
sonars. The ring configuration gave a 360% coverage as a polar plot. This ring was developed by
one of the first mobile robot manufacturers, Denning Robotics, and since then sonar rings are
often referred to as “Denning rings,” regardless of manufacturer. Besides providing
direct range measurements, the transducers were cheap, fast, and had terrific coverage.
Regardless of the maximum allowed range return (i.e., does the program ignore any reading over
3 meters?) and the width of the lobe, most computer programs divide the area covered by a sonar
into the three regions shown below.
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Region I is the region associated with the range reading. It is an arc, because the object that
returned the sound could be anywhere in the beam. The arc has a width, because there are some
resolution and measurement errors; the width of Region I is the tolerance. Region II is the area
that is empty.
If that area was not empty, the range reading would have been shorter. Region III is the area that
is theoretically covered by the sonar beam, but is unknown whether it is occupied or empty
because it is in the shadow of whatever was in Region I. Region IV is outside of the beam and
not of interest.
Although they are inexpensive, fast, and have a large operating range, ultrasonic sensors have
many shortcomings and limitations which a designer should be aware of. Ultrasonic sensors rely
on reflection, and so are susceptible to specular reflection.
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Consider a ring of multiple sonars. Suppose the sonars fire (emit a sound) at about the same time.
Even though they are each covering a different region around the robot, some specularly
reflected sound from a sonar might wind up getting received by a completely different sonar.
The receiving sonar is unable to tell the difference between sound generated by itself or by its
peers. This source of wrong reading is called cross-talk, because the sound waves are getting
crossed. Most robot systems stagger the firing of the sonars in a fixed pattern of four sonars, one
from each quadrant of the ring) at a time.
This helps some with cross-talk, but is not a complete or reliable solution. If the sonar sound
frequency and firing rate can be changed (which is generally not the case),
then sophisticated aliasing techniques can be applied.
Q. Infrared (IR)
Infrared sensors are another type of active proximity sensor. They emit nearinfrared
energy and measure whether any significant amount of the IR light is returned. If so, there is an
obstacle present, giving a binary signal. IR sensors have a range of inches to several feet,
depending on what frequency of light is used and the sensitivity of the receiver. The simplest IR
proximity sensors can be constructed from LEDs, which emit light into the environment
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and have a range of 3-5 inches.
In more sophisticated IR sensors, different IR bands can be selected or modulated to change the
signal-to-noise ratio. This typically ensures that an object in range doesn’t absorb the light,
causing the sensor to miss the presence of the object.
Bump and feeler sensors
Feelers or whiskers can be constructed from sturdy wires. Bump sensors are usually a protruding
ring around the robot consisting of two layers. Contact with an object causes the two layers to
touch, creating an electrical signal. In theory, the sensitivity of a bump sensor can be adjusted
for different contact pressures; some robots may want a “light” touch to create a signal rather
than a “heavier” touch.
Placement of bump sensors is a very important issue. The bump sensors on a Nomad 200 base
clearly protect the robot only from low obstacles not perceivable by sonar. The Denning mobile
robot platforms built in the 1980’s used a bump sensor that looked much like a thick piece of
gray tape. Denning mobile robots look like a fat version of the Nomad 200’s, and the bump
sensor is wrapped around the cowling of the robot at the waist level.
Unfortunately, in certain turning configurations, the wheels extend beyond the cowling. In those
situations, the bump sensor is totally useless in preventing the expensive synchro-drive
mechanism from being damaged in a collison. Feeler sensors are whiskers or antennae, only not
as sensitive as those on animals.
Q. Cryptarithmetic
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Notes by Santosh Sir... 9867211982/8976381939...AARYA CLASSES...www.aaryaclasses.co.in
Q. PROLOG
A logic programming language makes it possible to write algorithms by augmenting logical
sentences with information to control the inference process. Prolog is by far the most widely
used logic programming language. Its users number in the hundreds of thousands. It is used
primarily as a rapid-prototyping language and for symbol-manipulation tasks such as writing
compilers and parsing natural language. It has also been used to develop expert system
applications in legal, medical, financial, and other domains.
A Prolog program has the following characteristics:
• A program consists of a sequence of sentences, implicitly conjoined. All variables have
implicit universal quantification, and variables in different sentences are considered distinct.
• Only Horn clause sentences are acceptable. This means that each sentence is either an
atomic sentence or an implication with no negated antecedents and an atomic consequent.
• Terms can be constant symbols, variables, or functional terms.
• Queries can include conjunctions, disjunctions, variables, and functional terms.
• Instead of negated antecedents in implications, Prolog uses a negation as failure operator:
a goal not P is considered proved if the system fails to prove P.
• All syntactically distinct terms are assumed to refer to distinct objects. That is, you cannot
assert A = B or A = F(x), where A is a constant. You can assert x = B or x - F(y), where x is
a variable.
• There is a large set of built-in predicates for arithmetic, input/output, and various system
and knowledge base functions. Literals using these predicates are "proved" by executing
code rather than doing further inference.
As an example, here is a Prolog program for the Member relation, given both in normal firstorder logic notation and in the format actually used by Prolog:
The designers of Prolog made a number of implementation decisions designed to provide a
simple, fast execution model:
• All inferences are done by backward chaining, with depth-first search. That means that
whenever there is a dead end in the attempt to prove a sentence, Prolog backs up to the
most recent step that has alternatives.
• The order of search through the conjuncts of an antecedent is strictly left to right, and
clauses in the knowledge base are applied in first-to-last order.
• The occur-check is omitted from the unification routine.
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Q. Partial order planning
Also refer notebook
Algorithm:
POP starts with a minimal partial plan, and on each step extends the plan by achieving a
precondition c of a step Sneed- It does this by choosing some operator—either from the existing
steps of the plan or from the pool of operators—that achieves the precondition. It records the
causal link for the newly achieved precondition, and then resolves any threats to causal links.
The new step may threaten an existing causal link or an existing step may threaten the new
causal link. If at any point the algorithm fails to find a relevant operator or resolve a threat, it
backtracks to a previous choice point.
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Q. What are the components of AI?
Research in AI has focused chiefly on the following components of intelligence: learning,
reasoning, problem-solving, perception, and language-understanding.
Learning
Learning is distinguished into a number of different forms. The simplest is learning by trial-anderror. For example, a simple program for solving mate-in-one chess problems might try out
moves at random until one is found that achieves mate.
The program remembers the successful move and next time the computer is given the same
problem and it is able to produce the answer immediately.
This Rote learning is relatively easy to implement on a computer. More challenging is the
problem of implementing generalization. Learning that involves generalisation leaves the learner
able to perform better in situations not previously encountered.
A program that learns past tenses of regular English verbs by rote will not be able to produce the
past tense of e.g. "jump" until presented at least once with "jumped", whereas a program that is
able to generalize from examples can learn the "add-ed" rule, and so form the past tense of
"jump" in the absence of any previous encounter with this verb. Sophisticated modern techniques
enable programs to generalise complex rules from data.
Reasoning
Through reason, it is easy to draw inferences appropriate to the situation. Inferences are
classified as either deductive or inductive. An example of the former is "Fred is either in the
museum or the cafe; he isn't in the cafe; so he's in the museum", and of the latter "Previous
accidents just like this one have been caused by instrument failure; so probably this one was
caused by instrument failure".
The difference between the two is that in the deductive case, the truth of the premises guarantees
the truth of the conclusion, whereas in the inductive case, the truth of the premiss gives support
to the conclusion that the accident was caused by instrument failure, but despite the truth of the
premises, the conclusion is in fact false.
There has been considerable success in programming computers to draw inferences, especially
deductive inferences. Reasoning involves drawing inferences that are relevant to the task or
situation. But one of the problem in front of AI is that of giving computers the ability to
distinguish the relevant from the irrelevant.
Problem-solving
Problems have the general form: given such-and-such data, find x. A huge variety of types of
problems are addressed in AI. Some examples are:
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finding winning moves in board games;
identifying people from their photographs;
planning series of movements that enable a robot to carry out a given task.
Problem-solving methods divide into special-purpose and general-purpose.
A special-purpose method is made for a particular problem, and often exploits very specific
features of the situation in which the problem is embedded.
A general-purpose method is applicable to a wide range of different problems. One generalpurpose technique used in AI is means-end analysis, that involves the step-by-step reduction of
the difference between the current state and the goal state. The program selects actions from a
list of means--which in the case of, say, a simple robot, might consist of pickup, putdown,
moveforward, moveback, moveleft, and moveright--until the current state is transformed into the
goal state.
Perception
In perception the environment is scanned by means of various sense-organs, processes internal to
the perceiver, analyze the scene, it's features and relationships.
Analysis is complicated by the fact that one and the same object may present many different
appearances on different occasions, depending on the angle from which it is viewed
At present, artificial perception is sufficiently well advanced to enable a self-controlled car-like
device to drive at moderate speeds on the open road, and a mobile robot to roam through offices
searching for and clearing away empty soda cans.
One of the earliest systems to integrate perception and action was FREDDY, a stationary robot
with a moving TV 'eye' and a pincer. FREDDY was able to recognize a variety of objects and
could be instructed to assemble simple artifacts, such as a toy car, from components.
Language-understanding
A language is a system of signs having meaning by convention. Traffic signs, for example, form
a mini-language.
An important characteristic of full-fledged human languages, such as English, which
distinguishes them from, e.g. bird calls and systems of traffic signs, is their productivity. A
productive language is one that is rich enough to enable an unlimited number of different
sentences to be formulated within it.
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Q. INDUCTIVE
LEARNING
In supervised learning, the learning element is given the correct (or approximately correct) value
of the function for particular inputs, and changes its representation of the function to try to match
the information provided by the feedback. More formally, we say an example is a pair (x,f(x)),
where x is the input and/(jt) is the output of the function applied to x.
The task of pure inductive inference (or induction) is this: given a collection of examples of/,
return a function h that approximates. The function h is called a hypothesis.
In a), (x,y) points in the plane, where y = f(x), and the task is to find a function h(x) that fits the
points well.
In b) we have a piecewise-linear h function, while in Figure
In c) we have a more complicated h function. Both functions agree with the example points, but
differ on the values they assign to other x inputs.
In (d) we have a function that apparently ignores one of the example points, but fits the others
with a simple function. The true is unknown, so there are many choices for h, but without further
knowledge, we have no way to prefer (b), (c), or (d). Any preference for one hypothesis over
another, beyond mere consistency with the examples, is called a bias.
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In above algorithm, the REFLEX-LEARNING-ELEMENT updates a global variable, examples
,that holds a list of (percept, action) pairs. The percept could be a chess board position,
and the action could be the best move as determined by a helpful grandmaster.
When the REFLEX-PERFORMANCE-ELEMENT is faced with a percept it has been told about,
it chooses the corresponding action. Otherwise, it calls a learning algorithm INDUCE on the
examples it has seen so far. INDUCE returns a hypothesis h which the agent uses to choose an
action.
There are many variants on this simple scheme. For example, the agent could perform
incremental learning: rather than applying the learning algorithm to the entire set of examples
each time a new prediction is needed, the agent could just try to update its old hypothesis
whenever REFLEX-PERFORMANCE-ELEMENT makes no commitment to the way in which
the hypothesis is represented.
Q. Potentiometer
A potentiometer (or "pot," for short) is a manually-adjustable, variable resistor. It is commonly
used for volume and tone controls in stereo equipment. On the RoboBoard a 10k pot is used as a
contrast dial for the LCD screen, and the RoboKnob of the board is also a potentiometer.
In robotics, a potentiometer can be used as a position sensor. A rotary potentiometer (the most
common type) can be used to measure the rotation of a shaft. Gears can be used to connect the
rotation of the shaft being measured to the potentiometer shaft. It is easiest to use if the shaft
being measured does not need to rotate continuously (like the second hand on a clock), but rather
rotates back and forth (like the pendulum on a grandfather clock). Most potentiometers rotate
only about 270 degrees; some can be rotated continuously, but the values are the same on each
rotation. By using a gear ratio other than 1:1, the position of a shaft that rotates more than 270
degrees can be measured.
A potentiometer connected to a shaft and a lever can also be used to determine the distance to a
wall and to make the robot follow a path parallel to the wall. The lever, perhaps with a small
wheel on the end, would extend from the side of the robot and contact the wall; a rubber band
would provide a restoring force. If the robot moved closer to the wall, the lever would pivot,
turning the shaft and the potentiometer. The control program would read the resulting voltage
and adjust the robot steering to keep the voltage constant.
Electrical Data
Potentiometers have three terminals. The outer two terminals are connected to a resistor and the
resistance between them is constant (the value of the potentiometer). The center terminal is
connected to a contact that slides along the resistance element as the shaft is turned, so the
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resistance between it and either of the other terminals varies (one increases while the other
decreases).
Q. DC Motors
DC motors are widely used in robotics because of their small size and high energy output. They
are excellent for powering the drive wheels of a mobile robot as well as powering other
mechanical assemblies.
Several characteristics are important in selecting a DC motor.
Operating Voltage.
If batteries are the source of power for the motor, low operating voltages are desirable
because fewer cells are needed to obtain the specified voltage.
Operating Current.
The ideal motor would produce a great deal of power while requiring a minimum of
current. However, the current rating (in conjunction with the voltage rating) is usually a
good indication of the power output capacity of a motor.
Speed.
Usually this is specified as the speed in rotations per minute (RPM) of the motor when it
is unloaded, or running freely, at its specified operating voltage. Typical DC motors run
at speeds from one to twenty thousand RPM.
Torque.
The torque of a motor is the rotary force produced on its output shaft. When a motor is
stalled it is producing the maximum amount of torque that it can produce. Hence the
torque rating is usually taken when the motor has stalled and is called the stall torque.
Power.
The power of a motor is the product of its speed and torque. The power output is greatest
at about half way between the unloaded speed (maximum speed, no torque) and the
stalled state (maximum torque, no speed).
Q. Stepper Motors
The shaft of a stepper motor moves between discrete rotary positions typically separated by a
few degrees. Because of this precise position controllability, stepper motors are excellent for
applications that require high positioning accuracy. Stepper motors are used in X-Y scanners,
plotters, and machine tools, floppy and hard disk drive head positioning, computer printer head
positioning, and numerous other applications.
Stepper motors have several electromagnetic coils that must be powered sequentially to make the
motor turn, or step, from one position, to the next. By reversing the order that the coils are
powered, a stepper motor can be made to reverse direction. The rate at which the coils are
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respectively energized determines the velocity of the motor up to a physical limit. Typical
stepper motors have two or four coils.
Q. Inductor
Inductive proximity sensors are widely used in the modern high speed process control
environment for the detection, positioning and counting of ferrous and non-ferrous metal objects.
Due to the method of construction and superior performance of inductive sensors, they are
increasingly used to replace the traditional limit switch, thus upgrading speed and reliability of
existing machinery.
Principle of Operation
Inductive proximity sensors respond to ferrous and non - ferrous metal objects. They will also
detect metal through a layer of non - metal material. An inductive sensor consists of an oscillator
circuit (ie. the sensing part) and an output circuit including a switching device (eg. transistor or
thyristor), all housed in a resin encapsulated body. An essential part of the oscillator circuit is the
inductance coil creating a magnetic field in front of the sensing face. When the magnetic field is
disturbed, the output circuit responds by either closing the output switch (normally open version
type NO) or by opening the output switch (normally closed version type NC).
Q. Capacitor
Capacitive sensors are often successfully used in applications which cannot be solved with other
sensing techniques. Capacitive sensors respond to a change in the dielectric medium surrounding
the active face and can thus be tuned to sense almost any substance. Capacitive sensors can, also,
sense a substance through a layer of glass, plastic or thin carton.
Some typical applications for capacitive sensors are:
1. Level control of non-conductive liquids (oil, alcohol, fuel).
2. Level control of granular substances (flour, wheat, sugar).
3. Sensing substances through a protective layer (eg. glass).
Q. LVDT
Robots and robotic arms rely on sensors to determine range of motion or force applied to an
object. Attached to a robotic arm, the Macro Sensors Miniature LVDT Linear Position Sensor
detects position change when an actuator moves the robotic arm, providing an analog voltage
signal proportional to the amount of motion, to a controller that makes appropriate adjustments
based on programmable set points.
With the data provided by the sensor, the controller can determine when the robotic arm should
stop or slow down.
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For example, on a pick and place machine, if the robotic arm exceeded its travel distance, it
could drive right through the board it intended to populate. Macro Sensors miniature LVDT
Linear Position Sensors evaluate the robotic arm movement for optimum performance.
The compact size and highly accurate output of Macro Sensors CD 375 Series of Miniature
LVDT Sensors make the linear position sensors ideal for providing displacement feedback in
different robotic applications.
In addition to industrial robots, Macro Sensors CD 375 miniature LVDTs are suitable as an
integral part of devices such as hydraulic actuators, servo valves, medical equipment and other
small mechanisms. A compact 3/8” diameter design and lightweight low mass core make the
small contactless position sensors ideal for applications having high dynamic response
requirements such as ATMs, copy machines, plastic injection molding machines and automatic
inspection equipment.
Q. What are the reactive robotic characteristics
A reactive robotic system tightly couples perception to action without the use of intervening
abstract representations or time history. Purely reactive systems are at one extreme of the robotic
systems spectrum. Reactive robotic systems have the following characteristics:
o
o
Behaviors serve as the basic building blocks for robotic actions. A behavior in these
systems typically consists of a simple sensori motor pair, with the sensory activity
providing the necessary information to satisfy the applicability of a particular low-level
motor reflex response.
Use of explicit abstract representational knowledge is avoided in the generation of a
response. Purely reactive systems react directly to the world as it is sensed, avoiding the
need for intervening abstract representational knowledge.
This obviates the need for an ``accurate'' (in the sense of true-to-life) world model. This
could be of particular value in
- dynamic
- hazardous
- complex
worlds - wherever an accurate model would be difficult (time-consuming) or impossible
to calculate and/or rapid response is required. (Constructing abstract world models is a
time-consuming and error-prone process and thus reduces the potential correctness of a
robot's action in all but the most predictable worlds.[RA])
o
Animal models of behavior often serve as a basis for these systems.
1. Biology provides an existence proof that many of the tasks we would like our
robots to undertake are indeed doable.
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2. the biological sciences, such as neuroscience, ethology, and psychology, have
elucidated various mechanisms and models that may be useful in
"operationalizing'' robots.
3. These systems are inherently modular from a software design perspective. This
enables a reactive robotic system designer to expand his robot's competency by
adding new behaviors without redesigning or discarding the old.
This accretion of capabilities over time and resultant reusability is very useful for
constructing increasingly more complex robotic systems.
Q. Forward and Backward chaining
The Generalized Modus Ponens rule can be used in two ways. We can start with the sentences in
the knowledge base and generate new conclusions that in turn can allow more inferences to be
made. This is called forward chaining. Forward chaining is usually used when a new fact is
added to the database and we want to generate its consequences. Alternatively, we can start with
something we want to prove, find implication sentences that would allow us to conclude it, and
then attempt to establish their premises in turn. This is called backward chaining, because it
uses Modus Ponens backwards. Backward chaining is normally used when there is a goal to be
proved.
Forward Chaining
Forward chaining is normally triggered by the addition of a new fact p to the knowledge base. It
can be incorporated as part of the TELL process, for example. The idea is to find all implications
that have p as a premise; then if the other premises are already known to hold, we can add the
consequent of the implication to the knowledge base, triggering further inference
The FORWARD-CHAIN procedure makes use of the idea of a renaming. One sentence is
a renaming of another if they are identical except for the names of the variables. For example,
Likes(x, IceCream) and Likes(y, IceCream) are renamings of each other because they only differ
in the choice of x or y, but Likes(x, x) and Likes(x, y) are not renamings of each other.
We also need the idea of a composition of substitutions.
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Backward chaining
Backward chaining is designed to find all answers to a question posed to the knowledge base.
Backward chaining therefore exhibits the functionality required for the ASK procedure. The
backward-chaining algorithm BACK-CHAIN works by first checking to see if answers can be
provided directly from sentences'in the knowledge base. It then finds all implications whose
conclusion unifies with the query, and tries to establish the premises of those implications, also
by backward chaining. If the premise is a conjunction, then BACK-CHAIN processes the
conjunction conjunct by conjunct, building up the unifier for the whole premise as it goes.
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Q. Conversion to Normal Form
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