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Vision-Based Motion Control of Robots

Vision-Based Motion Control of Robots. Azad Shademan Guest Lecturer CMPUT 412 – Experimental Robotics Computing Science, University of Alberta Edmonton, Alberta, CANADA. desired. Left Image. Right Image. current. Vision-Based Control. B. B. A. A. A. B. Vision-Based Control.

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Vision-Based Motion Control of Robots

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  1. Vision-Based Motion Control of Robots Azad Shademan Guest Lecturer CMPUT 412 – Experimental Robotics Computing Science, University of Alberta Edmonton, Alberta, CANADA

  2. desired Left Image Right Image current Vision-Based Control B B A A A B A. Shademan. CMPUT 412, Vision-based motion control of robots

  3. Vision-Based Control Left Image Right Image B B B A. Shademan. CMPUT 412, Vision-based motion control of robots

  4. Vision-Based Control • Feedback from visual sensor (camera) to control a robot • Also called “Visual Servoing” • Is it any difficult? Images are 2D, the robot workspace is 3D 2D data  3D geometry A. Shademan. CMPUT 412, Vision-based motion control of robots

  5. Eye-to-Hand e.g.,hand/eye coordination Eye-in-Hand Where is the camera located? A. Shademan. CMPUT 412, Vision-based motion control of robots

  6. Visual Servo Control law • Position-Based: • Robust and real-time pose estimation + robot’s world-space (Cartesian) controller • Image-Based: • Desired image features seen from camera • Control law entirely based on image features A. Shademan. CMPUT 412, Vision-based motion control of robots

  7. Position-Based Desired pose Estimated pose A. Shademan. CMPUT 412, Vision-based motion control of robots

  8. Image-Based Desired Image feature Extracted image feature A. Shademan. CMPUT 412, Vision-based motion control of robots

  9. x1 x3 x4 x2 q=[q1 … q6] This Jacobian is important for motion control. Visual-motor Equation Visual-Motor Equation A. Shademan. CMPUT 412, Vision-based motion control of robots

  10. B A A B Visual-motor Jacobian Joint space velocity Image space velocity A. Shademan. CMPUT 412, Vision-based motion control of robots

  11. Image-Based Control Law • Measure the error in image space • Calculate/Estimate the inverse Jacobian • Update new joint values A. Shademan. CMPUT 412, Vision-based motion control of robots

  12. Desired Image feature Extracted image feature Image-Based Control Law A. Shademan. CMPUT 412, Vision-based motion control of robots

  13. Jacobian calculation • Analytic form available if model is known. Known model  Calibrated • Must be estimated if model is not known Unknown model  Uncalibrated A. Shademan. CMPUT 412, Vision-based motion control of robots

  14. Image Jacobian (calibrated) • Analytic form depends on depth estimates. Camera Velocity • Camera/Robot transform required. • No flexibility. A. Shademan. CMPUT 412, Vision-based motion control of robots

  15. Image Jacobian (uncalibrated) • A popular local estimator: Recursive secant method (Broyden update): A. Shademan. CMPUT 412, Vision-based motion control of robots

  16. Relaxed model assumptions Traditionally: Local methods No global planning  Difficult to show asymptotic stability condition is ensured  The problem of traditional methods is the locality. Model derived analytically Global asymptotic stability  Optimal planning is possible  A lot of prior knowledge on the model  Calibrated vs. Uncalibrated • Global Model Estimation (Research result) • Optimal trajectory planning  • Global stability guarantee  A. Shademan. CMPUT 412, Vision-based motion control of robots

  17. Synopsis of Global Visual Servoing • Model Estimation (Uncalibrated) • Visual-Motor Kinematics Model • Global Model • Extending Linear Estimation (Visual-Motor Jacobian) to Nonlinear Estimation • Our contributions: • K-NN Regression-Based Estimation • Locally Least Squares Estimation A. Shademan. CMPUT 412, Vision-based motion control of robots

  18. Key idea: using only the previous estimation to estimate the Jacobian RLS with forgetting factor Hosoda and Asada ’94 1st Rank Broyden update: Jägersand et al. ’97 Exploratory motion: Sutanto et al. ‘98 Quasi-Newton Jacobian estimation of moving object: Piepmeier et al. ‘04 Key idea: using all of the interaction history to estimate the Jacobian Globally-Stable controller design Optimal path planning Local methods don’t! Local vs. Global A. Shademan. CMPUT 412, Vision-based motion control of robots

  19. x1 ? 3 NN q2 q1 K-NN Regression-based Method q2 q1 A. Shademan. CMPUT 412, Vision-based motion control of robots

  20. x1 ? q2 KNN(q) q1 Locally Least Squares Method (X,q) A. Shademan. CMPUT 412, Vision-based motion control of robots

  21. Experimental Setup • Puma 560 • Eye-to-hand configuration • Stereo vision • Features: projection of the end-effector’s position on image planes (4-dim) • 3 DOF for control A. Shademan. CMPUT 412, Vision-based motion control of robots

  22. Measuring the Estimation Error A. Shademan. CMPUT 412, Vision-based motion control of robots

  23. Global Estimation Error A. Shademan. CMPUT 412, Vision-based motion control of robots

  24. With increasing noise level, the error decreases Noise on Estimation Quality KNN LLS A. Shademan. CMPUT 412, Vision-based motion control of robots

  25. Effect of Number of Neighbors A. Shademan. CMPUT 412, Vision-based motion control of robots

  26. Presented two global methods to learn the visual-motor function LLS (global) works better than the KNN (global) and local updates. KNN suffers from the bias in local estimations Noise helps system identification Conclusions A. Shademan. CMPUT 412, Vision-based motion control of robots

  27. Eye-in-Hand Simulator A. Shademan. CMPUT 412, Vision-based motion control of robots

  28. Eye-in-Hand Simulator A. Shademan. CMPUT 412, Vision-based motion control of robots

  29. Eye-in-Hand Simulator A. Shademan. CMPUT 412, Vision-based motion control of robots

  30. Eye-in-Hand Simulator A. Shademan. CMPUT 412, Vision-based motion control of robots

  31. Mean-Squared-Error A. Shademan. CMPUT 412, Vision-based motion control of robots

  32. Task Errors A. Shademan. CMPUT 412, Vision-based motion control of robots

  33. Questions? A. Shademan. CMPUT 412, Vision-based motion control of robots

  34. Position-Based • Robust and real-time relative pose estimation • Extended Kalman Filter to solve the nonlinear relative pose equations. • Cons: • EKF is not the optimal estimator. • Performance and the convergence of pose estimates are highly sensitive to EKF parameters. A. Shademan. CMPUT 412, Vision-based motion control of robots

  35. What kind of nonlinearity? IEKF Overview of PBVS 2D-3D nonlinear point correspondences T. Lefebvre et al. “Kalman Filters for Nonlinear Systems: A Comparison of Performance,” Intl. J. of Control, vol. 77, no. 7, pp. 639-653, May 2004. A. Shademan. CMPUT 412, Vision-based motion control of robots

  36. EKF Pose Estimation yaw pitch roll State variable Process noise Measurement noise Measurement equation is nonlinear and must be linearized. A. Shademan. CMPUT 412, Vision-based motion control of robots

  37. Visual-Servoing Based on the Estimated Global Model A. Shademan. CMPUT 412, Vision-based motion control of robots

  38. Control Based on Local Models A. Shademan. CMPUT 412, Vision-based motion control of robots

  39. Estimation for Local Methods • In practice: Broyden 1st-rank estimation, RLS with forgetting factor, etc. A. Shademan. CMPUT 412, Vision-based motion control of robots

  40. A. Shademan. CMPUT 412, Vision-based motion control of robots

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