CPG based self-adapting multi-DOF robotic arm control

Recently, biologically inspired control approaches for robotic systems that involve the use of central pattern generators (CPGs) have been attracting considerable attention owing to the fact that most humans or animals move and walk easily without explicitly controlling their movements. Furthermore, they exhibit natural adaptive motions against unexpected disturbances or environmental changes without considering their kinematic configurations. Inspired by such novel phenomena, this paper endeavors to achieve self-adapting robotic arm motion. For this, biologically inspired CPG based control is proposed. In particular, this approach deals with crucial problems such as motion generation and repeatability of the joints emerged remarkably in most of redundant DOF systems. These problems can be overcome by employing a control based on artificial neural oscillators, virtual force and virtual muscle damping instead of trajectories planning and inverse kinematics. Biologically inspired motions can be attained if the joints of a robotic arm are coupled to neural oscillators and virtual muscles. We experimentally demonstrate self-adaptation motions that that enables a 7-DOF robotic arm to make adaptive changes from the given motion to a compliant motion. In addition, it is verified with real a real robotic arm that human-like movements and motion repeatability are satisfied under kinematic redundancy of joints.