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A Modular Mobile Robotic
Platform As An Educational
Tool In Computer Science
And Engineering
CCCT ‘03
Andrey Shvartsman, Maurice Tedder, and Chan-Jin
Chung*
Department of Math and Computer Science
Lawrence Technological University
Southfield, 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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ChanJin Chung, Ph.D.
Changing for the Better
Assistant Professor
Founder and Organizer of Robofest
Dept. of Math and Computer Science, Lawrence Tech University
21000 West 10 Mile Road, Southfield, MI 48075-1058
248-204-3504
248-204-3518 Fax
chung@ltu.edu
www3.ltu.edu/~chung
www.robofest.net
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Introducing CogitoBot
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CogitoBot II
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Why Robotics in Computer
Science & Engineering Classes
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• 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
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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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Potential Obstacles in
introducing Robotics in CS Class
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• 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
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• A brain (or brains)
• Body: physical chassis that holds other
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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
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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
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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•
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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
Handy Board
• Up to two LogiTech Web Cameras
• WAAS (wide area augmentation system)
enabled GPS Receiver
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Block Diagram
Main Control
Module (PC)
USB Hub
Microcontroller
(68HC11-Based Handy Board)
USB-to-Serial
Interface
Battery
12V 7ampH
Fuse (500mA)
Fuse (10A)
Logitech
Camera
Remote E-Stop
(RF Module)
Vantec Motor
Driver Board
Left
Motor
GPS Navigation
Module
Sensors
Manual E- Stop
Infrared
Distance
Sensors
Polaroid
Sonar
Servo
Motor
Right
Motor
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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
Width
Height
Weight (Without Payload)
Weight Distribution (Left/Right/Rear)
Motor RPM
Motor Voltage
Motor Stall Current
Motor Stall Torque
Motor Power Output
Max. Speed
Gear Ratio
Wheel diameter
Traversable Incline
Battery Life
Waypoint Accuracy
Obstacle Detection Distance
Maximum IR Sensors Distance
Maximum Sonar Distance
Reaction Time
3 ft
1.33 ft
2.5 ft
53 lbs
41% / 41% / 18%
188 RPM
24V
4.5Amps
11 ft-lbs
0.1 hp
~ 1 MPH
4:1
8 in.
18 deg
1 Hour
< 10 ft
8 ft
< 3 ft
7 ft
50 ms
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Applications of the robot
platform
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RoboWaiter
RoboHelper
RoboTennis
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• 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,
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• 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
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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
Obstacle Position
&
Obstacle width
Right Lane
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Fuzzy Inference System
Speed for
Motor R
Lane center
position
Obstacle center
position
Fuzzy Inference System
FIS
Speed for
Motor L
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FIS Rules
Obstacle 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
Lane Center
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Membership functions for lane center
position
Far left
-6
middle
left
right
Far right
-2.5
3
6.5
10
14
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5
6
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Membership functions Obstacle Edge
Position
No obstacle
-1
0
2
left
2.5
5
middle
6
4
right
No obstacle
9.5
5
12
2
13
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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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How we become a independent
professional expert?
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1. Supervised learning; learning from
instruction
2. Study and memorization
3. Tests and exams, if fail, go to 2
4. On-the-job training (field test) until
satisfied
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ANN Training Paradigm for
CogotoBot
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// 1. supervised training
Gather 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 memorization
Evolve an initial ANN using the training dataset;
// 3. Exam and tests
Repeat 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 Results
Team
Distance
Place
Completed 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
U of Cincinnati - BearCat III
220.0 ft
4th
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?
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• 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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Demo: Obstacle Avoidance
while following dashed lanes
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Questions?
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