1. Robot Vision SS 2007 Matthias Rüther 1 ROBOT VISION Lesson 9: Robots & Vision Matthias Rüther

2. Robot Vision SS 2007 Matthias Rüther 2 Contents  Visual Servoing –Principle –Servoing Types

3. Robot Vision SS 2007 Matthias Rüther 3 Visual Servoing  Vision System operates in a closed control loop.  Better Accuracy than „Look and Move“ systems Figures from S.Hutchinson: A Tutorial on Visual Servo Control

4. Robot Vision SS 2007 Matthias Rüther 4 Visual Servoing  Example: Maintaining relative Object Position Figures from P. Wunsch and G. Hirzinger. Real-Time Visual Tracking of 3-D Objects with Dynamic Handling of Occlusion Real-Time Visual Tracking of 3-D Objects with Dynamic Handling of Occlusion

5. Robot Vision SS 2007 Matthias Rüther 5 Visual Servoing  Camera Configurations: End-Effector MountedFixed Figures from S.Hutchinson: A Tutorial on Visual Servo Control

6. Robot Vision SS 2007 Matthias Rüther 6 Visual Servoing  Servoing Architectures Figures from S.Hutchinson: A Tutorial on Visual Servo Control

7. Robot Vision SS 2007 Matthias Rüther 7 Visual Servoing  Position-based and Image Based control –Position based: Alignment in target coordinate system The 3D structure of the target is rconstructed The end-effector is tracked Sensitive to calibration errors Sensitive to reconstruction errors –Image based: Alignment in image coordinates No explicit reconstruction necessary Insensitive to calibration errors Only special problems solvable Depends on initial pose Depends on selected features target End-effector Image of target Image of end effector

8. Robot Vision SS 2007 Matthias Rüther 8 Visual Servoing  EOL and ECL control –EOL: endpoint open-loop; only the target is observed by the camera –ECL: endpoint closed-loop; target as well as end-effector are observed by the camera EOL ECL

9. Robot Vision SS 2007 Matthias Rüther 9 Visual Servoing  Position Based Algorithm: 1.Estimation of relative pose 2.Computation of error between current pose and target pose 3.Movement of robot  Example: point alignment p1p1 p2p2

10. Robot Vision SS 2007 Matthias Rüther 10 Visual Servoing  Position based point alignment  Goal: bring e to 0 by moving p 1 e = |p 2m – p 1m | u = k*(p 2m – p 1m )  p xm is subject to the following measurement errors: sensor position, sensor calibration, sensor measurement error  p xm is independent of the following errors: end effector position, target position p 1m p 2m d

11. Robot Vision SS 2007 Matthias Rüther 11 Visual Servoing  Image based point alignment  Goal: bring e to 0 by moving p 1 e = |u 1m – v 1m | + |u 2m – v 2m |  u xm, v xm is subject only to sensor measurement error  u xm, v xm is independent of the following measurement errors: sensor position, end effector position, sensor calibration, target position p1p1 p2p2 c1c1 c2c2 u1u1 u2u2 v1v1 v2v2 d1d1 d2d2

12. Robot Vision SS 2007 Matthias Rüther 12 Visual Servoing  Example Laparoscopy Figures from A.Krupa: Autonomous 3-D Positioning of Surgical Instruments in Robotized Laparoscopic Surgery Using Visual Servoing

13. Robot Vision SS 2007 Matthias Rüther 13 Visual Servoing  Example Laparoscopy Figures from A.Krupa: Autonomous 3-D Positioning of Surgical Instruments in Robotized Laparoscopic Surgery Using Visual Servoing