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Vision System in real-time tracking

Vision System in real-time tracking. 2002. 8. 12 MAI LAB Ryu Mi Wyun. Real-time vision-based tracking control of an unmanned vehicle. Kok-Meng LEE, Zhi Zhou, Robert Blenis and Erik Blasch* *The George W. Woodruff School of Mechanical Engineering,

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Vision System in real-time tracking

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  1. Vision System in real-time tracking 2002. 8. 12 MAI LAB Ryu Mi Wyun

  2. Real-time vision-based tracking control of an unmanned vehicle Kok-Meng LEE, Zhi Zhou, Robert Blenis and Erik Blasch* *The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0405, USA Mechatronics Vol.5, No. 8 (1995) 973 - 997

  3. Contents • Introduction • Integrated vision-based control • Dynamic modeling and control strategy • Integrated vision-based tracking control system • Experimental investigation • Conclusions

  4. Introduction (1/2) Increased Demand for Autonomous Vehicles Unattended Factories Material Handling Warehouse Operation Hazardous Environment Requires real-time position and/or velocity feedback Using Machine Vision to provide the feedback info. Advances In Manufacturing Automation

  5. Introduction (2/2) Target - Space Drive - Space Vision system • where the vehicle is located and oriented • where the vehicle is controlled, with an implementation of a path planning mechanism • Major difficulties • Slow and/or expensive visual feedback • Complicated dynamics and control with constraints in ‘target space’ • The development of a practical method for implementing real-time vision based control of autonomous vehicles.

  6. Integrated vision-based control - Idea • Vision-based vehicle control systems • Spaced-fixed vision systems : range of motion is restricted/ more than one vision system is required • On-the-Vehicle (On-board) vision systems • Takes pictures periodically while moving • Captures the patterns within its fields of view • Stores and analyzes the images • Returns the vehicle’s locations

  7. Integrated vision-based control – Fiducial patterns • Landmarks to determine the vehicle’s location • using a feature set • made of retro-reflective material to eliminate noise • An illustrative example • , , • Two distinct distances between two elements : • Pattern orientation :

  8. Integrated vision-based control – Fiducial patterns

  9. Integrated vision-based control – Pattern recognition • Real world Coordinate • Image Coordinate

  10. Dynamic modeling and control strategy Obtain the complete set of dynamic models Propose a path planning mechanism Outline the control strategy development • Three-wheeled vehicle • Vehicle is modeled as a rectangular plate of dimensions • The axes of the driving motor shafts are perfectly aligned / • are located directly below the transversal axis of the vehicle

  11. Dynamic modeling and control strategy • The torques applied at the wheels • Masses of the wheel and the vehicle • The radius of the rear wheels • Vehicle Dynamics : using Lagrangian formulation position of the center of gravity of the vehicle, orientation of the axis .. Equations of motion for the vehicle are..

  12. Dynamic modeling and control strategy • Path Planning : Implementation of simplified controller design

  13. Dynamic modeling and control strategy* • Utilizing the visual feedback and modern digital control technology • Control functions will be implemented using DSP and/or computers • - design the controllers in the discrete-time domain • Digital tracking controller : Discretize state-space representation of the motor dynamics

  14. Dynamic modeling and control strategy* • Utilizing the visual feedback and modern digital control technology • Control functions will be implemented using DSP and/or computers • - design the controllers in the discrete-time domain • Digital tracking controller : Discretize state-space representation of the motor dynamics • Regulator with reduced-order observer : Obtain the specified time response of the system

  15. Dynamic modeling and control strategy* • Utilizing the visual feedback and modern digital control technology • Control functions will be implemented using DSP and/or computers • - design the controllers in the discrete-time domain • Digital tracking controller : Discretize state-space representation of the motor dynamics • Regulator with reduced-order observer : Obtain the specified time response of the system • Digital tracking filter : To follow an arbitrary desired time-varying trajectory

  16. Integrated vision-based tracking control system • Laboratory prototype system • RS170-based systems • : Require pixel data to be stored in a video buffer • : Increased time and require video buffer size • FIVS – Providing the feedback information • : integrates imaging sensors, control, illumination, signal processing and data communication • : By directly transferring the A/D converter output to the DSP

  17. Integrated vision-based tracking control system • ViTra (Vision-based Tracking system) • : Three wheel configuration with two rear driving wheels & ball-joint-like front wheel • : Rear wheels are geared to two D.C motors • Using the two motors • : reduces the backlash, friction and inertia.. • Ball-joint-like front wheel • : to maneuver in all directions without slip • ViTra integrates the three-wheeled autonomous vehicle, an on-board FIVS, a ceiling fiducial board, a DSP board with A/D and D/A converters, and an Intel 486 as central computer

  18. Experimental investigation • 30 Patterns are placed 9.75 ft above the CCD lens • For different vehicle positions and orientations around a predetermined trajectory.

  19. Experimental investigation • Fig.11-12 give the corresponding plots. • Slightly off from the known positions. • - average position deviation from the true position is 2.92 and 2.63 in. • Due to the level of the CCD lens and the lighting conditions etc.

  20. Experimental investigation • Desired circular path with radius of 20 in. • A number of tests / Two typical paths are recorded. • Overall real paths follow the desired ones. • Some data points far from desired paths • - mismatch of the FIVS update rate & moving speed • - can be reduced by optimizing the pattern recognition • Reducing the trajectory discrepancy - unmodeled friction - ceiling board not perfectly leveled - approximation in path planning

  21. Conclusions • ViTra uses a DSP-based flexible integrated vision system(FIVS) : low cost, computational efficiency, flexibility.. • Using fiducial patterns and corresponding pattern recognition • Can use the digital controllers and observer to enhance the performance • Sample circular trajectory tracking experiments illustrated the possibility  Optimizing the fiducial patterns, Conducting various trajectory tracking tests

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