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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?