Biologically inspired control algorithm for an unified motion of whole robotic arm-hand system

Biologically inspired control approaches have been attracted much attention as alternatives in recent time, for efficiently solving problems in controlling multi-DOF robotic systems, since most human beings or animals exhibit their behaviors in a natural way without explicit computation. Also, they show natural adaptive behaviors irrespective of unexpected external forces or changes of environment. This work is inspired from these novel features. Thus, a self-adapting robotic arm-hand control is proposed exploiting a control scheme based on central pattern generators (CPGs). Instead of a trajectory planning and inverse kinematics problem, this work endeavors to exploit robotic systems coupled with neural oscillators and virtual forces with joint velocity damping. We demonstrate self-adapting motions without the ill-posedness from extensive simulations that enable a robotic arm-hand to make adaptive changes from the given motion to a compliant motion. In addition, it is verified that reaching-to-grasping motion is possible by adopting only transit points sustaining motion repeatability under kinematic redundancy of joints.