Robot manipulator self identification for surrounding obstacle detection
Obstacle detection plays an important role for robot collision avoidance and motion planning. This paper focuses on the study of the collision prediction of a dual arm robot based on a 3D point cloud. Firstly, a self identification method is presented based on the over segmentation approach and the forward kinematic model of the robot. Secondly, a simplified 3D model of the robot is generated using the segmented point cloud. Finally, a collision prediction algorithm is proposed to estimate the collision parameters in real time. Experimental studies using the KinectR sensor and the BaxterR robot have been performed to demonstrate the performance of the proposed algorithms.
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