Abstract
Kinematic redundancy allows a manipulator to have more joints than required and could make the robot more flexible in the obstacles avoidance. This chapter first introduces the concept of kinematic redundancy. Then, a human robot shared controller is developed for teleoperation of redundant manipulator by developing an improved obstacle avoidance strategy based on the joint space redundancy of the manipulator. Next, a self-identification method is described based on the 3D point cloud and the forward kinematic model of the robot. By implementing a space division method, the point cloud is segmented into several groups which represent the meaning of the points. A collision prediction algorithm is then employed to estimate the collision parameters in real-time. The experiment using the Kinect sensor and the Baxter robot has demonstrated the performance of the proposed algorithms.
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