Collision avoidance method for hand-arm robot using both structural model and 3D point cloud

In recent years, obstacle detection has been achieved using laser rangefinders or the Kinect sensor. The point cloud contains the point data of a known object such as a hand-arm robot. In this paper, to remove the known object from the point cloud, we propose a removal method that compares the depth buffer between virtual space and real space. Then a nearest neighbor search with a kd-tree is performed. Also, the closest point pair is calculated with the GJK algorithm between the components of the hand-arm robot using the structural model of the known object. Using both distances, we also propose a controller based on a control performance index containing collision avoidance information. The collision avoidance method is implemented into the slave hand-arm robot for the teleoperated system. Finally, the effectiveness of the proposed method is demonstrated experimentally.