Post on 27-Mar-2015
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A Modular Mobile Robotic Platform As An
Educational Tool In Computer ScienceAnd Engineering
CCCT ‘03Andrey Shvartsman, Maurice Tedder, and
Chan-Jin Chung*Department of Math and Computer Science
Lawrence Technological UniversitySouthfield, Michigan 48075, USA
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Andrey Shvartsman, Maurice Tedder, and
Chan-Jin Chung*
Dept. of Computer Science Lawrence Technological U.Southfield, Michigan 48075
USA
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Assistant Professor
Founder and Organizer of RobofestFounder and Organizer of Robofest
Dept. of Math and Computer Science, Lawrence Tech University21000 West 10 Mile Road, Southfield, MI 48075-1058
248-204-3504 248-204-3518 Faxchung@ltu.edu www3.ltu.edu/~chung
www.robofest.net
ChanJin Chung, Ph.D.ChanJin Chung, Ph.D.Changingfor the Better
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Introducing CogitoBot
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CogitoBot II
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6Why Robotics in Computer Science & Engineering Classes • Encompass the rich nature of integrated
systems that includes mechanical, electrical, and computational components
• Putting theories into practice• Motivation• Fun
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ACM-IEEE 2001 CS Curricular • Fundamental issues in Intelligent Systems
(1)• Search and constraint satisfaction (5) • Knowledge representation and reasoning (4) • Advanced search • Advanced knowledge representation and
reasoning • Multi-Agents • Natural language processing • Machine learning and neural networks • AI planning systems
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8Potential Obstacles in introducing Robotics in CS Class • Complex• Un-reliable• Expensive
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Our Basic Strategies
• Use a laptop for the brain of the robot
• Modular and Expandable• Exchangeable (New brain, if you buy
a new laptop!)• Affordable; Cost effective (less than
$1,000 w/o laptop)• Standard programming interface• Multiple programming language
support: C++, Java, etc
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Fundamental Components of Autonomous Robots• A brain (or brains)• Body: physical chassis that holds
other pieces• Actuators: allows to move. Motors,
hydraulic pistons, lamps, etc• Sensors• Power source• Communication
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1st generation LTU laptop Robot in 2002!
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The Brain
• On board CPU?• Desktop?• Palm Pilot?• Our choice: Laptop & Handy Board
Laptop: Pentium III 800Mhz, LTU Laptop Handy Board: 2 MHz Motorola 68HC11
microprocessor, 32K static RAM with Analog and Digital I/O - Interface between sensors and laptop
• How to train/educate the brain?
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Robot Body
• Designed and built from off-the-shelf components
• The main body was constructed from MDF 0.75 inches in thickness
• The upper body was constructed from particle board 0.25 inches in thickness
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Body: Drive Train and Gearing• Front-wheel drive• 8-inch lawn mower wheels• 51 Teeth on each wheel• Stationary axle• Pivoting: wheels rotate freely on the
axis in both directions. Zero-turn radius steering
• Coupled to 13 tooth gear• 4:1 gear ratio, higher torque• Gear mounted directly on motor shaft
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Actuators: Motors and Motor Control• 12V DC worm gear bi-directional
high-torque motors • Motor Shaft Rotates at 120 RPMs• Controlled by a dual channel 30 Amp
driver board• Commands sent through laptop
parallel port• A servo motor to rotate a sonar
sensor (180 degrees)
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Sensors
• 2 IR Distance sensors• 1 Sonar sensor
• Up to two LogiTech Web Cameras
• WAAS (wide area augmentation system) enabled GPS Receiver
Handy Board
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Main Control Module (PC)
GPS Navigation Module
USB Hub
USB-to-Serial Interface
LogitechCamera
Microcontroller(68HC11-Based Handy Board)
Sensors
Polaroid Sonar
ServoMotor
InfraredDistance Sensors
Battery12V 7ampH
Remote E-Stop (RF Module)
Vantec Motor Driver Board
LeftMotor
RightMotor
Fuse (10A)
Fuse (500mA)
Block Diagram
Manual E- Stop
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Power Source
• One 12V 7 Amp battery for motors, motor boards, and Handy Board
• Can last for an hour• Manual and remote emergency stop
switches are wired• Laptop and GPS unit both have their
own rechargeable batteries
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Communication
• Wireless card on the laptop• The laptop is connected to a virtual
private network through a wireless LAN system
• MS Speech SDK
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Performance Spec.Length 3 ftWidth 1.33 ftHeight 2.5 ftWeight (Without Payload) 53 lbsWeight Distribution (Left/Right/Rear) 41% / 41% / 18%Motor RPM 188 RPMMotor Voltage 24VMotor Stall Current 4.5AmpsMotor Stall Torque 11 ft-lbsMotor Power Output 0.1 hpMax. Speed ~ 1 MPHGear Ratio 4:1Wheel diameter 8 in.Traversable Incline 18 degBattery Life 1 HourWaypoint Accuracy < 10 ftObstacle Detection Distance 8 ftMaximum IR Sensors Distance < 3 ftMaximum Sonar Distance 7 ftReaction Time 50 ms
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Applications of the robot platform• RoboWaiter• RoboHelper• RoboTennis• …
•IGVC competition
• How to train the brain? • Software Control Architecture
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IGVC
• International Intelligent Ground Vehicle Competition
• Sponsored by DOD, TACOM, DARPA, GM, …
• Obstacle avoidance while following lanes
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IGVC Courses
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CogitoBot Control Technologies• Vision processing for two cameras• Fuzzy Inference System using
Sugeno model• Written in C++
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CogitoBot Vision Processing
• Image frame from 2 cameras are concatenated to form a single frame that is much wider
• This image frame is then formatted to a grid of 4X12
• Each cell is processed to check for lane and obstacle presence
• Information from all the cells are combined to know the position of Left and Right lanes and the Obstacle Width and position.
• These information are used as inputs to the Fuzzy Inference System
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Sample Image Frame without Obstacle
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Sample Image Frame without Obstacle
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Sample Image Frame with Obstacle
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CogitoBot Vision Processing
Left Lane Right LaneObstacle Position &
Obstacle width
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Fuzzy Inference System
Fuzzy Inference SystemFIS
Lane center position
Obstacle center position
Speed for Motor R
Speed for Motor L
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Obstacle Center
Lane Center
No obstacle
Left Middle Right
Far left Hard left Left Left Slight left
Left Left Left Left Slight left
Middle Straight Slight right Left/right Slight left
Right Right Right Hard right Right
Far right Hard right Slight right Right Slight right
FIS Rules
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-6 -2.5 3 6.5 10 14 18
7 6 5 6 7
Far left left middle right Far right
Membership functions for lane center position
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-1 0 2.5 6 9.5 12 13
2 5 4 5 2
No obstacle left middle right No obstacle
Membership functions Obstacle Edge Position
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CogitoBot II Characteristics
• One CCD camera• Gathering training data by teaching
the robot• Training of Artificial Neural Network
using Evolutionary Computation, ES(1+1) with modified 1/5 rule
• Robot Behaves as if it has a Brain
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Robot Trainer
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The Fuzzy Evolutionary Artificial Neural Network
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43How we become a independent professional expert?1. Supervised learning; learning from
instruction2. Study and memorization3. Tests and exams, if fail, go to 24. On-the-job training (field test) until
satisfied
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ANN Training Paradigm for CogotoBot// 1. supervised trainingGather initial ‘basic’ training dataset labeled by only human trainer; (Use k-NN to verify, because the human trainer may make mistakes. Also redundancy is checked.)// 2. study and memorizationEvolve an initial ANN using the training dataset;// 3. Exam and testsRepeat until satisfied{ present a new pattern to the robot’s ANN; // note that the robot is not moving if (ANN’s label human trainer’s label) { add the pattern with human’s label to the training dataset after verifying using k-NN; Evolve ANN using previous weight values and the updated training dataset; }}// 4. On-the-job training: field trial. The robot is now moving
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11th IGVC Competition ResultsTeam Distance
Completed
Place Awarded
Virginia Tech - Optimus 500.0 ft 1st
Virginia Tech - Zieg 291.0 ft 2nd
University of Florida - TailGator
272.0 ft 3rd
Lawrence Tech U – CogitoBot II
220.0 ft 4th
U of Cincinnati - BearCat III 193.0 ft 5th
Lawrence Tech U - CogitoBot
134.0 ft 6th
U of Colorado Denver - CUGAR IV
106.42 ft 7th
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Lawrence Tech IGVC’03 team
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Interested in getting a CogitoBot?• Please contact Lawrence Tech
Robotics Lab• Basic CogitoBot with one Web
Camera• chung@ltu.edu
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Demo: Line Following
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Questions?