Yang, Woosung Bae, Ji-Hun Oh, Yonghwan Chong, Nak Young You, Bum-Jae Oh, Sang-Rok 2024-01-19T12:39:27Z 2024-01-19T12:39:27Z 2022-03-07 2010 2153-0858 https://pubs.kist.re.kr/handle/201004/115761 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. English IEEE CPG based Self-adapting Multi-DOF Robotic Arm Control Conference 10.1109/IROS.2010.5651377 1 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp.4236 - 4243 IEEE/RSJ International Conference on Intelligent Robots and Systems 4236 4243 US Taipei, TAIWAN 2010-10-18 IEEE/RSJ 2010 INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2010) 000287672001095 2-s2.0-78651481806 Proceedings Paper