1. Capstone Design Project Plan Team SAUSAGES Ryan Campbell Anne Carrier Gonzalo Gonzalez Bryan Grider Steve Kerkmaz Ziad Mohieddin EE 401 – EE Design I Instructor – Dr. Mohan April 13, 2003

2.  IGVC Standards  0 to 8 kph speed range  2 to 5 m radius turns  3 m following distance  No modification to Lead Vehicle  Course Obstacles  UDM Standards  0 to 8 kph speed range  2 to 5 m radius turns  1 m following distance  Allowed to mount targets and sensors on Lead Vehicle  Obstacle Free Problem Statement and Project Objectives

3. Solution Strategy Chassis Software SensorsPlatooning Strategy Strategy ComputationalHardware

4. Solution Strategy Chassis Software SensorsPlatooning Strategy Strategy ComputationalHardware

5. Power Wheels Eliminator  Advantages  Pre-Built Systems  Large Available Area  Easily Modifiable  Inexpensive  Disadvantages  Traction  Steering Control

6.  Traction Control  Mount Conveyor Belt to Area of Contact with Ground Chassis Modifications  Steering System  Gear and Chain System  Servo or Window Motor

7. Solution Strategy Chassis Software SensorsPlatooning Strategy Strategy ComputationalHardware

8. Shuttle Barebone  Inexpensive  Interchangeable parts  Small package  2 GB Maximum memory capacity

9. I/O Card  Counter/Timers  Used to create PWM signals  Digital Lines  Used for Ultrasound  A/D converters  Possibly used for backup sensors

10. Solution Strategy Chassis Software SensorsPlatooning Strategy Strategy ComputationalHardware

11. Ultrasound Sensors  Donated by BOSCH  Once in diagnostic mode, outputs 1 byte every 25 ms  1 bit = 1 cm of distance

12. CMUcam SX28 Microcontroller interfaced with a OV6620 omnivision CMOS camera. $ 109.00 OVERVIEW

13. CMUcam Vision Sensor 80 * 143 image Resolution. Dump images (color blobs). Track images at 17 frames / seconds. Find centroid of image. Adjust the camera’s image properties. Ability to control a servo or use one Digital I/O pin. RS-232 serial or TTL data communication. MAIN FEATURES AND FUNCTIONALITIES

14. CMUcam Vision Sensor Response to ambient and florescent light. Servo’s sensitivity to image tracking. DISADVANTAGES

15. CMUcam Vision Sensor Eyes for vehicle navigation. Preliminary steps for steering control. RELEVANCE TO OUR PROJECT

16. IR Sensors

17. How does it work?  The black and white disk is for creating pulses.  The reflected beam on the black segment will cause the sensor to output a signal (pulse).  Each pulse corresponds to a specific distance traveled.  The number of pulses in a given time will determine the speed.

18. Speed  Number of pulses = (n)  Distance = (n * x)  Speed = Dist. / Time.

19. Solution Strategy Chassis Software SensorsPlatooning Strategy Strategy ComputationalHardware

20. Matlab/Simulink  xPC Target  Real-Time Workshop  Stateflow Coder

21. xPC Target HOST PC (desktop) Development of the algorithm Non-real time testing of the algorithm Tuning of the algorithm using real-time signals coming from the Target PC HOST PC (desktop) Development of the algorithm Non-real time testing of the algorithm Tuning of the algorithm using real-time signals coming from the Target PC TARGET PC (on vehicle) Real-time simulation of the algorithm Runs the final algorithm on vehicle On board implementation of the algorithm TARGET PC (on vehicle) Real-time simulation of the algorithm Runs the final algorithm on vehicle On board implementation of the algorithm

22. Real-Time Workshop

23. Stateflow Coder  Designs complex control systems based on finite state machine theory.  Represents the system logically and can eliminate the unnecessary states within the system.  The basic structure of the flow diagrams is created in Stateflow by complex if…then statements which allow the program to jump between two different algorithms.

24. Solution Strategy Chassis Software SensorsPlatooning Strategy Strategy ComputationalHardware

25. Platooning Strategy  The Platooning Strategy must use our sensors to allow us to follow our project constraints.  Our algorithm was developed using two algorithms.

26. Algorithm for Purely Autonomous Vehicle

27. Algorithm for Vehicle Platooning Using a Video Camera

28. Chosen Algorithm

29. Final Thoughts…

30. Questions?