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Vision-Based Reach-To-Grasp Movements

Vision-Based Reach-To-Grasp Movements. From the Human Example to an Autonomous Robotic System. Alexa Hauck. Context. MODEL of Hand-Eye Coordination. ANALYSIS of human reaching movements. SYNTHESIS of a robotic system. Special Research Program “Sensorimotor”.

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Vision-Based Reach-To-Grasp Movements

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  1. Vision-Based Reach-To-Grasp Movements From the Human Example to an Autonomous Robotic System Alexa Hauck

  2. Context MODEL of Hand-Eye Coordination ANALYSIS of human reaching movements SYNTHESIS of a robotic system Special Research Program “Sensorimotor” • C1: Human and Robotic Hand-Eye Coordination • Neurological Clinic (Großhadern), LMU München • Institute for Real-Time Computer Systems, TU München

  3. The Question is ... control strategy representation catching reaching How to use whichvisualinformation for motioncontrol?

  4. State-of-the-art Robotics Look-then-move: (visual feedforward control) Visual Servoing: (visual feedback control) • easy integration with path planning • only little visual information needed • sensitive against model errors • model errors can be compensated • convergence not assured • high-rate vision needed Impressive results ... but nowhere near human performance!

  5. The Human Example Separately controlled hand transport: • almost straight path • bell-shaped velocity profile Experiments with target jump: • smooth on-line correction of the trajectory Experiments with prism glasses: • on-line correction using visual feedback • off-line recalibration of internal models • Use of visual information in spatial representation • Combination of visual feedforward and feedback ... but how ?

  6. New Control Strategy

  7. Example: Point-to-point

  8. Example: Target Jump

  9. Example: Target Jump

  10. Example: Target Jump

  11. Example: Multiple Jumps

  12. Example: Multiple Jumps

  13. Example: Double Jump

  14. Hand-Eye System position target & hand Image Interpretation Motion Planning object model object model Models Hand-Eye System & Objects features trajectory Image Processing Robot Control sensor model arm model images commands Robot

  15. The Robot: MinERVA CCD cameras pan-tilt head manipulator with 6 joints

  16. Robot Vision Target corresponding points 3D Bin. Stereo Hand corresponding points

  17. Example: Reaching

  18. Example: Reaching

  19. Example: Reaching

  20. Model Parameters HALCON HALCON Calibration • Arm: • geometry, kinematics • 3 parameters • Arm-Head Relation: • coordinate transformation • 3 parameters • Head-Camera Relations: • coordinate transformations • 4 parameters • Cameras: • pinhole camera model • 4 parameters (+ rad. distortion) manufacturer measuring tape

  21. Use of Visual Feedback corr mean max 0 8.9cm 20cm 1 Hz 0.4cm 1cm

  22. Example: Vergence Error

  23. Example: Compensation

  24. Summary • New control strategy for hand-eye coordination • Extension of a biological model • Unification of look-then-move & visual servoing • Flexible,economic use of visual information • Validation in simulation • Implementation on a real hand-eye system

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