Automatic Grasping Region Extraction Using Shape Profile Based and Geometrical Features Approach

Saint Saint Pyone, Wut Yi Win, Aung Myat San


Many applications of robotics include the grasping and manipulation of objects. Working in assembly robotic environments, the robot has to accurately not only locate the part but also to recognize it in readiness for grasping. In order to determine a grasping position, it is necessary to recognize the types of object, and detect portions which are suitable for grasp. According to get the important data clearly and correctly from the images, the detection and extraction methods are essential. This paper is mainly focused on the method of extracting the PCA and Shaped Profile with geometrical feature. Our proposed method is the combination of shapes based approach with the ratio and hole features. The proposed system has been tested successfully to a dataset of 336 images for seven types of common hand tools and achieved good accuracy and less computation complexity for 2D images by using a single camera. The overall recognition accuracy of PCA method with geometrical feature approach is 69.0476% on the same set of test images whereas overall accuracy of shape profile based method with geometrical feature approach is 97.9167%. Base on the experiment, this system is robust for the industrial robots for grasping tasks. This paper intends to implement machine vision system for industrial robotic grasping tasks. 


accuracy; grasping region extraction; PCA features; Shaped profile feature; machine vision.

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