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Vision and Obstacle Avoidance In Cartesian Space

This overview discusses the critical role of vision and obstacle avoidance in dynamic workspaces inhabited by multiple robots and humans. It emphasizes the challenges faced, such as unpredictable environments and the necessity for robust object identification techniques. The article delves into vision systems, camera calibration, and key methods like edge and corner detection that support effective navigation and trajectory generation. It also touches on impedance controllers and the importance of real-time control loops for ensuring system stability during obstacle avoidance.

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Vision and Obstacle Avoidance In Cartesian Space

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  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 • Cameras • Light sensitive chips • Light • Visible spectrum • Color • Red • Blue • Green

  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.

  8. Camera frame is our target in the camera perspective in the Cartesian. Finding is not easy. Problems with depth

  9. Scaling the x and y coordinate by z we can make an image point Let M Where Pixel point =principal length Principal point 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 are intrinsic camera properties that can only be determined through camera calibration, since each camera has different properties. is an extrinsic property that will change depending on the orientation of the camera. Best resource for camera calibration is http://www.vision.caltech.edu/bouguetj/calib_doc/ Once the calibration is done the intrinsic properties don’t need to be calculated again.

  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 Vision controls the and in Effect changes entire system response and can easily make system unstable. Takes extensive knowledge of controller and system response.

  14. An alternative approach Control Loop A Vision Model Loop

  15. Questions?

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