Download An Autonomous Industrial Load Carrying Vehicle

Advance in Electronic and Electric Engineering.
ISSN 2231-1297, Volume 4, Number 2 (2014), pp. 169-178
© Research India Publications
http://www.ripublication.com/aeee.htm
An Autonomous Industrial Load Carrying Vehicle
Pradnya T. Chauhan1 and Ganesh Rahate2
1
Department of Electronics & Telecommunication M.E. (E&TC),
Pune University, Akurdi, Pune, India.
2
Department of Electronics & Telecommunication M.E. (E&TC),
Pune University, Akurdi, Pune, India.
E-mail: [email protected], [email protected]
Abstract
The demand toward automation has changed the way that today’s
warehouses and distribution centers operate. An ordinary industrial (or
warehouse) human-operated forklift needs to be improved to achieve
an efficient automated industrial task in the material handling process.
With the rapid advances in sensor and computer technologies, the
opportunities for developing an autonomous forklift are immense. I
had gone through many articles in which a system configuration uses
vision, laser ranger finder, sonar, etc. for autonomous navigation.
Autonomous forklift handling systems is one of the most interesting
researches in the last decades. In order to successfully demonstrate the
working capability of unmanned autonomous forklift, the ability of it
to detect and navigate objects autonomously becomes the critical part
in research. In this paper, the development of an unmanned
autonomous forklift is discussed. In which I am going to focus on the
use of LPC 2138, LCD, motor to operate forklift, RF transceiver and
different sensors such as position sensor, line detecting sensor and ultra
sonic sensor for autonomous navigation and it material handling
process.
Keywords: Autonomous forklift, LPC2138, motors, RF transceiver,
Sensors.
1. Introduction
One long-standing goal of research in human-robot interaction is achieving safe and
effective command and control mechanisms for mobile robots. This goal becomes
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increasingly important as robots are deployed into human-occupied environments. We
discuss our approach toward this goal in the context of robotic forklifts tasked with
autonomously performing warehouse operations in an indoor environment. The
process of loading, unloading and transporting of materials is one of the key issues for
every production site and has a great impact on costs The material flow within the
product manufacturing cycle i.e. from the work benches to the storage is very
expensive and can be optimized in many companies, so that the efforts to find
favorable and flexible systems continues to be of great importance. The demand
toward a higher level of automation has changed the way that today’s warehouses and
distribution centers operate.
In recent years, there have been an increasing number of researches on the subject
of unmanned vehicle navigation. Particularly, for the development of automatic
industrial forklift, several research results can be found The works presented in
Garibotto et al. (1997) and Seelinger and Yoder (2006) describe the development of
pallet engagement algorithm by using a vision system, while a different approach for
localization and picking up the pallet by using a laser scanner is presented in Lecking
et al. (2006). An extensive description for automating an industrial forklift together
with a road following control algorithm by using a camera is presented in Rodríguez et
al. (1998). This paper focuses on the development of a fully autonomous forklifter in
which I will used ARM7 as a main controller, ultrasonic sensor for detecting obstacles
and also for detecting empty space in rack, IR sensor used as a for line follower, LCD
is used to display data. RF data transceiver is used to transmit the data to
administration and receive the data from administration. Position sensor (limit
switches) is used to sensed that whether the forklift keep load properly in the rack.
There are two different motors used DC motor can operate fork lifter and bipolar
stepper motor is used for forklift movement control.
2. Related Work
The development of the automatic industrial forklift [1,2,3,4,6,7]. The works presented
in [3,7] describe the development of the pallet engagement algorithm by using vision
system, while works in [6] present a different approach for localization and pick-up of
the pallet by using a laser scanner. The optimization of the vision system for the
navigation of autonomous forklift are presented in [2,7], while the works in [5]
describe the development of a simulator for optimization of the multi-AGV operation
in industrial environments. An extensive description for industrial forklift automation
as well as a road-following control algorithm using a camera is presented in [4]. Fong
et al. [8] address the problem of designing an interface that can be used to control a
ground robot on uneven terrain with minimal user training. Their PdaDriver system
uses images from a user-selectable camera for situational awareness, and allows the
supervisor to teleoperate the vehicle with stylus gestures using either a twoaxis virtual
joystick displayed on the screen or by specifying a desired trajectory segment by
clicking waypoints on the image. Our interface also makes use of gestures drawn on
images as a means of commanding the robot, though for tasks other than navigation.
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Another similarity lies in the extension to the collaborative control paradigm [12],
which emphasizes the importance of interaction between the robot and operator to
facilitate situational awareness. Similar to the PdaDriver system, Keskinpala et al. [18]
describe a PDA-based touch-screen interface for mobile robot control through which
the user teleoperates a ground robot. In addition to providing views from a vehiclemounted camera, the interface allows the user to view raw LIDAR (laserrange scanner)
and sonar returns, either projected on the camera image or on a synthesized overhead
view of the robot. The latter view is intended to facilitate teleoperation within cluttered
environments, where a forward-facing camera image would provide insufficient
situational awareness. Our approach is different, in that we render contextual
knowledge at object level (e.g., pedestrian detections) as opposed to rendering raw
sensor data, which subsequent user studies [17] have shown to add to the user’s
workload during teleoperation. Sakamoto et al. [23] utilize gestures made on the world
to command an indoor robot. Through a limited set of strokes, the user can give simple
navigation directives by drawing on a bird’s-eye view of the robot’s environment
displayed on a tablet computer. Originating from downward-facing cameras mounted
to the ceiling, the interface requires significant environment preparation; this limits the
vehicle’s operating region to the cameras’ field of view. Other investigators have also
shown the utility of enabling a teleoperator to switch between first-person and thirdperson views of the workspace [11]. Existing research related to multimodal robot
interaction [15] exploits a combination of vision and speech as input One important
element of joint human-robot activity is the ability of individual agents to detect each
others’ cues. Such cues are important aids in interpreting intent or other internal state;
for example, eye contact has been shown to play an important role in pedestrian safety
[14]. In autonomous vehicles, such cues are often missing by default. Matsumaru et. al.
[20] explores several methods for communicating intended future motion of a mobile
robot, including the use of synthetic eyes. The eyes indicate future motion, but not
perceived objects of interest in the world
3. Figures and Tables
LCD
Motor for forklift robo
movement control
RF Data Transceiver
Position sensor for forklift
height
LPC 2138
Line detection sensor
Motors to operate
forklift
Ultra sonic sensor
Fig. 1: Block diagram of autonomous forklift system.
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3.1 ARM7 Description
The LPC2131/2132/2138 microcontrollers are based on a 32/16 bit ARM7TDMI-S™
CPU with real-time emulation and embedded trace support, that combines the
microcontroller with 32 kB, 64 kB and 512 kB of embedded high speed Flash memory.
A 128- bit wide memory interface and a unique accelerator architecture enable 32-bit
code execution at maximum clock rate. For critical code size applications, the
alternative 16-bit Thumb® Mode reduces code by more than 30 % with minimal
performance penalty. Due to their tiny size and low power consumption, these
microcontrollers are ideal for applications where miniaturization is a key requirement,
such as access control and point-of-sale. With a wide range of serial communications
interfaces and on-chip SRAM options of 8/16/32 kB, they are very well suited for
communication gateways and protocol converters, soft modems, voice recognition and
low end imaging, providing both large buffer size and high processing power. Various
32-bit timers, single or dual 10-bit 8 channel ADC(s), 10-bit DAC, PWM channels and
47 GPIO lines with up to nine edge or level sensitive external interrupt pins make these
microcontrollers particularly suitable for industrial control and medical systems.
3.2 LCD Description
Whenever you come across a LCD that looks like it has 16 connectors it is most likely
using a HD44780 controller. These devices provide the same pinouts making them
relatively easy to work with. The LCD uses a parallel interface meaning that we will
need many pins from our raspberry pi to control it. In this tutorial we will use 4 data
pins (4-bit mode) and two control pins. The data pins are straight forward. They are
sending data to the display (toggled high/low). We will only be using write mode and
not reading any data. The register select pin has two uses. When pulled low it can send
commands to the LCD (like position to move to, or clear the screen). This is referred to
as writing to the instruction or command register. When toggled the other way the
register select pin goes into a data mode and will be used to send data to the screen.
The read/write pin will be pulled low (write only) as we only want to write to the LCD
based on this setup. The enable pin will be toggled to write data to the registers.
Display mode: FSTN / Negative / Transmissive, View direction: 6 O’clock, 5×8 dotes
with cursor, 16 character*2 lines display, 4-bit or 8-bit MPU interface and Backlight:
LED sidelight (white).
3.3 IR Sensors
Sensors are basically electronic devices which are used to sense the changes that occur
in their surroundings. The change may be in color, temperature, moisture, sound, heat
etc. They sense the change and work accordingly. In IR sensor the there is emitter and
detector. Emitter emits the IR rays and detector detects it. The IR sensor basically
consists of three components IR LED (emitter), Photodiode (detector), Op-Amp.
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3.3.1 Working
We know that the white surface reflects all the radiations falls on it whereas the black
color absorbs them. When the supply is given to IR sensor, LED starts emitting light
radiations. If the surface is of white color then it reflects all the radiations. As these
radiations starts falling on the photodiode which is connected in reverse bias, the
resistance of the photodiode starts decreasing rapidly and the voltage drop across the
diode also decreases. The voltage at Pin 3 starts increases, as it reaches just beyond the
voltage of Pin 2 the comparator gives high output. In case of the black surface, LED
emits light but it is not reflected by the surface, so the photodiode detects nothing and
its resistance remains infinite. Hence the comparator gives low output. White surface–
Comparator output is high. Black surface– Comparator output is low.
3.4 Ultrasonic sensor Transmitter and Receiver
Before transmit the ultrasonic wave, there is a part which is ultrasonic wave generator
that function to generate ultrasonic wave. In that part, there is timing instruction means
for generating an instruction signal for intermittently providing ultrasonic waves. This
signal will send to an ultrasonic wave generator for generating ultrasonic waves based
on the instruction signal from said timing instruction means (transform electrical
energy into sound wave). After ultrasonic wave was produced, ultrasonic transmitter
transmits the ultrasonic waves toward a road surface to find out the obstacle. The range
that obstacle detected is depends on the range of ultrasonic sensors that used.
If the ultrasonic wave detect the obstacle, its will produce a reflected wave. An
ultrasonic receiver is used for receiving the ultrasonic waves reflected from the road
surface to generate a reception signal. There is ultrasonic transducer that will transform
back the sound wave to electrical energy. This signal amplified by an amplifier. The
amplified signal is compared with reference signal to detect components in the
amplified signal due to obstacles on the road surface. The magnitude of the reference
signal or the amplification factor of the amplifier is controlled to maintain a constant
ratio between the average of the reference signal and the average of the amplified
signal. Minimum range 10 centimeters, Maximum range 400 centimeters (4 Meters),
5V DC Supply voltage, 15 mA , Modulated at 40 kHz
3.5 DC motor
DC motors have many applications and used for multi-purpose applications. A
machine that converts dc power into mechanical energy is known as dc motor. Its
operation is based on the principle that when a current carrying conductor is placed in
a magnetic field, the conductor experiences a mechanical force. The direction of the
force is given by Fleming’s left hand rule. Parts of a DC Motor are a simple motor has
six parts Armature/ Rotor, Commutator, Brushes, Axle, Permanent Magnet, DC Power
supply.
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3.6 Limit Switch
Limit Switch Features are Amp rating 0.1 A to 25 A, Circuitry SPDT, SPNO, SPNC, Operating
force 0.7 oz max. to 14.6 oz max, Terminations quick connect, pc board, pcb straight angle left,
Actuators/Levers pin plunger, straight, short flag, roller, sim. roller, curved tip, loop, paddle , Voltage
125 Vac, 250 Vac, 277 Vac, Approvals UL, CSA, ENEC, Operating temperature -40 °C to 82 °C [-40 °F
to 180 °F], Contacts silver, silver cadmium oxide, gold and Housing material PCT polyester
thermoplastic.
3.7 Bipolar Stepper motor Features
NEMA 17 Frame Size(42 x 42mm), 1.8° Step Angle, High Torque - Up to 70 oz-in,
High Step Accuracy and Resolution, Low Vibration and Noise, Typical Applications
of Nema 17 Stepper Motor: Inject printers, Dispense Robot, Analytical and Medical
Instruments, Textile Equipment, Embroidery Machine, Precision Telescope
Positioning Systems, Stage Lighting and Robotics, With the highest possible torque
and also small dimensions (42 x 42 mm) the Nema 17 Stepper Motor series offers high
resolution and is employed with economically priced drives for precision applications.
These series Nema 17 Stepper Motor offer a great value without sacrificing quality and
were designed to offer the highest possible torque while minimizing vibration and
audible noise. A broad line of Stepper Motor windings and stack lengths are available
off-the-shelf, or the Stepper Motor can be customized to fit your machine
requirements. We can customize the Stepper Motor winding to perfectly match your
voltage, current, and maximum operating speed for maximum flexibility. Technique
parameter of Bipolar Stepper motor Step Angle Accuracy ±5%full step,no load,
Resistance Accuracy ±10%, Inductance Accuracy ±20%, Temperature Rise 80ͦ Max.
(rated current,2 phase on), Ambient Temperature -10ͦ -+50ͦ, Insulation Resistance
100MΩMin.500VDC, Dielectric Strength 500VAC for one minute.
3.8 RF Transceiver Descriptions
The CC2500 is a low-cost 2.4 GHz transceiver designed for very low-power wireless
applications. The circuit is intended for the 2400- 2483.5 MHz ISM (Industrial,
Scientific and Medical) and SRD (Short Range Device) frequency band. The RF
transceiver is integrated with a highly configurable baseband modem. The modem
supports various modulation formats and has a configurable data rate up to 500 kBaud.
CC2500 provides extensive hardware support for packet handling, data buffering, burst
transmissions, clear channel assessment, link quality indication, and wake-on-radio.
The main operating parameters and the 64-byte transmit/receive FIFOs of CC2500 can
be controlled via an SPI interface. In a typical system, the CC2500 will be used
together with a microcontroller and a few additional passive components. Low power
consumption., High sensitivity (type -104dBm), Programmable output power 20dBm~1dBm, Operation temperature range : -40~+85 deg C, Operation voltage:
1.8~3.6 Volts, Available frequency at : 2.4~2.483 GHz.
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4. Conclusion
While working on this project the expected result is well develop an autonomous
forklift which will navigate autonomously having ability to avoid obstacle while
moving forward. It will be able to control its speed and work on priority base. It will
detect the empty space in the rack, keep the load at its assign place and if a product is
not available in the stock it will inform to the administrator.
5. Acknowledgement
We take this opportunity to thank out Head of the Department Prof. Sheetal Bhandari
for her guidance and providing necessary facilities, which were indispensable in the
completion of this report. We are also thankful to our seminar guide Prof. Ganesh
Rahate and Seminar coordinator Prof. Varsha Harpale and all the staff members of
the Electronic and Telecommunication engineering department. We would like to
thank the college for providing the required magazines, books and access to the
internet for collecting the information related to the seminar. We thank our Principal,
Dr. A. M. Fulambarkar, who is a constant source of motivation for us. Finally, we
are also grateful to our friends for their valuable comments and suggestions.
References
Journal Papers
[1] H. Frohn and W. V. von Seelen, May (1989) “VISOCAR: an autonomous
industrial transport vehicle guided by visual navigation,” Proc. of IEEE
International Conference on Robotics and Automation, 2, pp. 1155- 1159.
[2] R. K. Miller, D. G. Stewart, W. H. Brockman, and S. B. Skaar, October
(1994) “A camera space control system for an automated forklift,” IEEE
Transactions on Robotics and Automation, 10(5), pp. 710-716.
[3] G. Garibotto, S. Masciangelo, M. Ilic and P. Bassino, November (1996)
“ROBOLIFT: a vision guided autonomous fork-lift for pallet handling,”
Proceedings of International Robots and Systems, pp. 656-663.
[4] F. J. Rodríguez, M. Mazo and M. A. Sotelo, May (1998) “Automation of an
industrial forklift truck, guided by artificial vision in open environment,”
Autonomous Robot, 5(2), pp. 215-231.
[5] K. Petrinec, Z. Kovačić, and A. Marozin, December (2003) “Simulator of
multi-AGV robotic industrial environments,” Proc. of IEEE International
Conference on Industrial Technology, 2, pp. 979-983.
[6] D. Lecking, O. Wulf and B. Wagner, September (2006) “Variable pallet pickup for automatic guided vehicles in industrial environments,” Proceedings of
IEEE Conference on Emerging Technologies and Factory Automation, pp.
1169-1174.
176
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17]
[18]
[19]
Pradnya T. Chauhan & Ganesh Rahate
M. Seelinger and J. D. Yoder, December (2006) “Automatic visual guidance
of a forklift engaging a pallet,” Robotics and Autonomous System, 54(12), pp.
1026-1038.
T. Fong, C. Thorpe, and B. Glass. PdaDriver, July (2003) A handheld system
for remote driving. In Proc. IEEE Int’l Conf. Advanced Robotics.
A. Adler and R. Davis. Speech and sketching, Aug. (2007) An empirical study
of multimodal interaction. In Proc. Euro graphics Workshop on Sketch based
Interfaces and Modeling, pages 83–90, Riverside, CA.
R. Davis , March (2002) Sketch understanding in design: Overview of work at
the MIT AI Lab. In Proc. AAAI Spring Symposium on Sketch Understanding,
pages 24–31, Stanford, CA. AAAI Press.
F. Ferland, F. Pomerleau, C.T. Le Dinh, and F. Michaud, March (2009)
Egocentric and exocentric teleoperation interface using real-time, 3D video
projection. In Proc. ACM/IEEE International Conference on Human-Robot
Interaction (HRI), pages 37–44.
T. Fong, C. Thorpe, and C. Baur, Aug. 2003 Multi-robot remote driving with
collaborative control. IEEE Transactions on Industrial Electronics, 50(4):699–
704.
J.R. Glass, November (2003) A probabilistic framework for segment-based
speech recognition. Computer Speech and Language, 17:137–152.
B. Hamilton-Baillie and P. Jones, May (2005) Improving traffic behaviour and
safety through urban design. Civil Engineering, 158(5):39–47.
H. Holzapfel, K. Nickel, and R. Stiefelhagen, October( 2004) Implementation
and evaluation of a constraint-based multimodal fusion system for speech and
3D pointing gestures. In Proc. International Conference on Multimodal
Interfaces (ICMI), pages 175–182, State College, PN.
M. Johnston, P.R. Cohen, D. McGee, S.L. Oviatt, J.A. Pittman, and I. Smith,
(1997)Unification-based multimodal integration. In Proc. Eighth conference
on European chapter of the Association for Computational Linguistics, pages
281–288, Morristown, NJ.
H. Kaymaz-Keskinpala and J.A. Adams, October (2004) Objective data
analysis for PDA-based human-robot interaction. In Proc. IEEE International
Conference on Systems, Man and Cybernetics (SMC), volume 3, pages 2809–
2814, The Hague, The Netherlands.
H. Kaymaz-Keskinpala, J.A. Adams, and K. Kawamura, October (2003)
PDA-based human-robotic interface. In Proc. IEEE Conference on Systems,
Man, and Cybernetics (SMC), volume 4, pages 3931–3936, Washington, DC.
T. Matsumaru, (2007) Mobile robot with preliminary-announcement and
indication function of forthcoming operation using flat-panel display. In Proc.
IEEE International Conference on Robotics and Automation (ICRA), pages
1774–1781.
An Autonomous Industrial Load Carrying Vehicle
177
[20] T. Matsumaru, K. Iwase, K. Akiyama, T. Kusada, and T. Ito, (2005) Mobile
robot with eyeball expression as the preliminary-announcement and display of
the robots following motion. Autonomous Robots, 18(2):231–246.
[21] T.Y. Ouyang and R. Davis, (2007) Recognition of hand drawn chemical
diagrams. In Proc. AAAI Conference on Artificial Intelligence, pages 846–
851.
[22] D. Perzanowski, A.C. Schultz, W. Adams, E. Marsh, and M. Bugajska, Jan.Feb. (2001)Building a multimodal human–robot interface. Intelligent Systems,
16(1):16–21.
[23] D. Sakamoto, K. Honda, M. Inami, and T. Igarashi, April (2009). Sketch and
run: A stroke-based interface for home robots. In Proc. International
Conference on Human Factors in Computing Systems (CHI), pages 197– 200,
Boston, MA.
[24] M. Skubic, D. Anderson, S. Blisard, D. Perzanowski, and A. Schultz, May
(2007). Using a hand-drawn sketch to control a team of robots. Autonomous
Robots, 22(4):399–410.
[25] B. Yu and S. Cai, (2003). A domain-independent system for sketch
recognition. In GRAPHITE ’03: Proc. 1st International Conference on
Computer graphics and Interactive Techniques in Australasia and South East
Asia, pages 141–146, New York, NY. ACM.
[26] Andrew Correa, Matthew R. Walter, Luke Fletcher, Jim Glass, Seth Teller,
Randall Davis, May (2011)” Multimodal Interaction with an Autonomous
Forklift” Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology Cambridge, MA, USA.
[27] Tua Agustinus Tamba, Bonghee Hong, and Keum-Shik Hong(2009)”A Path
Following Control of an Unmanned Autonomous Forklift” International
Journal of Control, Automation, and Systems.
[28] Kunihiko
Aoki,
Hideak
Furuno,
Jun
Nagaoka,
Koji
Furukawa(2013)”Autonomous Forklift Automatic Guided Vehicle” Hitachi
Review Vol. 62.
[29] Seth Teller, Matthew R. Walter, Matthew Antone, Andrew Correa, Randall
Davis, Luke Fletcher, Emilio Frazzoli, Jim Glass, Jonathan P. How, Albert S.
Huang, Jeong hwan Jeon, Sertac Karaman, Brandon Luders, Nicholas Roy,
Tara Sainath June-July (2011)” A Voice-Commandable Robotic Forklift
Working Alongside Humans in Minimally-Prepared Outdoor Environments”.
Books
1. Trevor Martin, The Insider’s Guide to the Philips ARM7-Based
Microcontrollers ISBN: 0-95499881 first published february 2005 First Reprint
April 2005.
2. Data books of ARM7/ARM9 J. Staunstrup and W. Wolf, editors,
Hardware/Software Co-Design: Principles and Practice, Kluwer Academic
Publishers, 1997.
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