Vision and Obstacle Avoidance In Cartesian Space.
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Transcript of Vision and Obstacle Avoidance In Cartesian Space.
![Page 1: Vision and Obstacle Avoidance In Cartesian Space.](https://reader033.fdocuments.in/reader033/viewer/2022061610/5697bfa41a28abf838c973b9/html5/thumbnails/1.jpg)
Vision and Obstacle Avoidance In Cartesian Space
![Page 2: Vision and Obstacle Avoidance In Cartesian Space.](https://reader033.fdocuments.in/reader033/viewer/2022061610/5697bfa41a28abf838c973b9/html5/thumbnails/2.jpg)
Why is Obstacle Avoidance Important
• Workspace can change unexpectedly• No prior knowledge of workspace• Multiple robots in workspace• Humans in workspace!
![Page 3: Vision and Obstacle Avoidance In Cartesian Space.](https://reader033.fdocuments.in/reader033/viewer/2022061610/5697bfa41a28abf838c973b9/html5/thumbnails/3.jpg)
Addressing the Issue
• Vision – Object Identification– Coordinate Transformation
• Control– Trajectory generator avoidance – Impedance controllers
![Page 4: Vision and Obstacle Avoidance In Cartesian Space.](https://reader033.fdocuments.in/reader033/viewer/2022061610/5697bfa41a28abf838c973b9/html5/thumbnails/4.jpg)
Vision Introduction
• Cameras– Light sensitive chips
• Light – Visible spectrum
• Color– Red– Blue– Green
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Image acquisition
• Grayscale
• Bayer
• Binary
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Common feature extraction techniques
• Edge Detection– Edge(image,method)
• Sobel• Prewitt• canny
• Corner detection– Corner(image)– SIFT– SURF
• Color schemed detection– Achieved through logic
![Page 7: Vision and Obstacle Avoidance In Cartesian Space.](https://reader033.fdocuments.in/reader033/viewer/2022061610/5697bfa41a28abf838c973b9/html5/thumbnails/7.jpg)
Color schemed detection demo
• Now that we have the pixel value from our image lets find the Cartesian coordinate of this object.
![Page 8: Vision and Obstacle Avoidance In Cartesian Space.](https://reader033.fdocuments.in/reader033/viewer/2022061610/5697bfa41a28abf838c973b9/html5/thumbnails/8.jpg)
Camera frame
is our target in the camera perspective in the Cartesian.
Finding is not easy.
Problems with depth
![Page 9: Vision and Obstacle Avoidance In Cartesian Space.](https://reader033.fdocuments.in/reader033/viewer/2022061610/5697bfa41a28abf838c973b9/html5/thumbnails/9.jpg)
Scaling the x and y coordinate by z we can make an image point Let
M
Where
=principal length
𝑃❑𝐶
Pixel point
Principal point
Pixel Origin
𝑣 𝑢
Transforming from 3d to 2d where the mapping is not one-to-one, i.e. unique inverse does not exist because of the depth
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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.
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Obstacle avoidanceVision
Vision based controller
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Haptic Geometries
Using basic geometries find the optimal path around the object and back to the normal trajectory.
![Page 13: Vision and Obstacle Avoidance In Cartesian Space.](https://reader033.fdocuments.in/reader033/viewer/2022061610/5697bfa41a28abf838c973b9/html5/thumbnails/13.jpg)
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.
![Page 14: Vision and Obstacle Avoidance In Cartesian Space.](https://reader033.fdocuments.in/reader033/viewer/2022061610/5697bfa41a28abf838c973b9/html5/thumbnails/14.jpg)
An alternative approach
A
Vision
Model Loop
Control Loop
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Questions?