CogitoBot IGVC Autonomous Vehicle From Lawrence Technological University MayJune 2003 - PowerPoint PPT Presentation

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CogitoBot IGVC Autonomous Vehicle From Lawrence Technological University MayJune 2003

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The robot body was designed and built from off-the-shelf components. ... 8-inch lawn mower wheels. 51 Teeth on each wheel. Stationary axle ... – PowerPoint PPT presentation

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Title: CogitoBot IGVC Autonomous Vehicle From Lawrence Technological University MayJune 2003


1
CogitoBot IGVC Autonomous Vehicle From Lawrence
Technological UniversityMay-June 2003
  • Team Name LTU Thinkers
  • By Andrey Shvartsman
  • Santosh Nair
  • Li Ping Chen
  • Date 05/31/2003

2
Hardware Design and Implementation
3
Robot Body
  • The robot body was designed and built from
    off-the-shelf components.
  • The main body was constructed from particle board
    0.75 inches in thickness.
  • The upper body was constructed from particle
    board 0.25 inches in thickness.

4
Drive Train and Gearing
  • Front-wheel drive
  • 8-inch lawn mower wheels
  • 51 Teeth on each wheel
  • Stationary axle
  • Wheels rotate freely on the axis in both
    directions
  • Coupled to 13 tooth gear
  • 41 gear ratio, higher torque
  • Gear mounted directly on motor shaft

5
Motors and Motor Control
  • 12V DC wormgear 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

6
Electrical System
  • 2 IR Distance sensors
  • 1 Sonar sensor
  • 2 LogiTech Cameras
  • HandyBoard 68HC11 Microcontroller
  • Interface between sensors and laptop

7
(No Transcript)
8
High-Level Software Architecture
9
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

10
Sample Image Frame without Obstacle
11
Sample Image Frame without Obstacle - II
12
Sample Image Frame with Obstacle
13
Fuzzy Inference System
Speed for Motor R
Lane center position
Fuzzy Inference System FIS
Obstacle center position
Speed for Motor L
14
FIS Rules
15
Membership functions - I
Far left
middle
right
Far right
left
-6 -2.5 3 6.5 10 14 18
7 6 5 6 7
Membership function for lane center position
16
Membership functions - II
middle
right
No obstacle
left
No obstacle
-1 0 2.5 6 9.5 12 13
2 5 4 5 2
Membership function of obstacle edge position
17
Questions?
Thanks for listening
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