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Motion Tracking & Position Acquisition

Motion Tracking & Position Acquisition. Final Presentation. Solomon Gates | William K. Grefe | Jay Michael Heidbreder | Jeremy Kolpak. Overview of Project Objective. Primary Goal Achieve accurate and precise motion of laser pointer directed at a locator beacon Secondary Goal

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Motion Tracking & Position Acquisition

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  1. Motion Tracking &Position Acquisition Final Presentation Solomon Gates | William K. Grefe | Jay Michael Heidbreder | Jeremy Kolpak

  2. Overview of Project Objective Primary Goal Achieve accurate and precise motion of laser pointer directed at a locator beacon Secondary Goal Obtain precise object position from sensor input

  3. Original Design Specifications • Object tracking velocity: • Object velocity of 10 mph • Pan/Tilt velocity of 10 radians per second • Object acquisition within 1 second • Distance to object: ½ft – 20ft • Range of motion: • Pan range of 180° • Tilt range of 90° • Target Acquisition Accuracy • ½” at a range of 20 ft (0.0021 radians) • 1/8” at a range of 6” (.021 radians) • Tracking Moving Object • ± 1” @ 20 ft (0.004 radians) • ± ¼” @ 6” (0.041 radians)

  4. Controller Design Process Simulate Desired Motors Simulate Plant (Linearized System) Designed PID Controllers Tested System (Real World) Designed Friction Compensation

  5. Designing a Suitable Controller • Linearized our simulated plant system • Estimated desired dampening and natural frequency values to achieve a suitable overshoot and settling time. • Created a PID controller • Simulated the PID controller input response with the linearized plant system.

  6. Real World Plant/Controller Testing • Initially our real world system did not react to the controller as the simulated system. • Real World friction compensation was initially non-existant (identify viscous and coulomb friction) • Simulated plant friction model was incorrect • Estimated system models were not completely accurate causing phase difference in system response

  7. Pan Comparison

  8. Pan Comparison

  9. Tilt Comparison

  10. Tilt Comparison

  11. Basic Friction Compensation System • Add coulomb compensation based on the change in encoder reading • This type of compensation can fail when the motor approaches the steady state value (stiction zone) • If at this point the encoder reading does not change, the coulomb compensation is not added and the motor does not move and for future readings the encoder will not change. • Basic Friction Point to Point Video

  12. Group 3 Friction Compensation System • Add coulomb compensation based on the difference between the current and desired position. • This will provide constant compensation until the controller acquires the desired position. • This can however cause oscillations for small movement and near the steady state value. • We fixed this by adding a dead zone to remove oscillations near steady state.

  13. Pan Point to Point Accuracy(Point to Point Video)

  14. Tilt Point to Point Accuracy

  15. Pan Motion Testing(Show Motion Tracking Video)

  16. Pan Motion Testing (Zoom)

  17. Tilt Motion Testing

  18. Tilt Motion Testing (Zoom)

  19. Sensor Design Beacon will be built from six ultrasonic transceivers to allow 360° range Three receivers received signal

  20. Sensor Problems • Radio frequency transmitter and receiver pair proved too complicated to implement on ARCS system • Ultrasonic transmitter and receivers were built and tested; devices shown to communicate with each other • No time remained to integrate transmitter with PIC microcontroller and MATLAB code with enough accuracy.

  21. Position Acquisition • Receivers built and tested to acquire a signal from the transmitter • MATLAB code used to calculate x,y,z position based upon simulated distance information from three simulated receivers • Position was then related to the given position of laser to generate angle values Da, Db, Dc x, y, z Θ1, Θ2 MATLAB Triangulation Routine MATLAB Angle Localization Routine Controller

  22. Performance Comparison

  23. Success & Challenges • It was challenging to relate the real world plant to the simulated model. We were able to achieve this in the end. • Creating the friction compensation was more of a challenge than we had expected, and in the end came up with a new way to handle this. This new system however, had its own drawbacks that we overcame. • Creating a sensor system from scratch. We were able to successfully create the components however time did not permit us to integrate and test them with our controller.

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