1. Vision and Obstacle Avoidance In Cartesian Space

2. Why is Obstacle Avoidance Important Workspace can change unexpectedly No prior knowledge of workspace Multiple robots in workspace Humans in workspace!

3. Addressing the Issue Vision – Object Identification – Coordinate Transformation Control – Trajectory generator avoidance – Impedance controllers

4. Vision Introduction

5. Image acquisition Grayscale Bayer Binary

6. Common feature extraction techniques Edge Detection – Edge(image,method) Sobel Prewitt canny Corner detection – Corner(image) – SIFT – SURF Color schemed detection – Achieved through logic

7. Color schemed detection demo Now that we have the pixel value from our image lets find the Cartesian coordinate of this object.

9. Pixel Origin Transforming from 3d to 2d where the mapping is not one-to-one, i.e. unique inverse does not exist because of the depth

10. Camera Calibration

11. Obstacle avoidance Vision Vision based controller

12. Haptic Geometries Using basic geometries find the optimal path around the object and back to the normal trajectory.

13. Vision Impedance Controller

14. An alternative approach A Vision Model Loop Control Loop

15. Questions?