Design and Validation of New Acceleration-Level Repetitive Motion Planning Scheme for Redundant Robot Manipulators

Repetitive motion planning (RMP) has become part and parcel of redundant robot manipulators. Various RMP schemes with different formulations have been developed at the level of joint velocity or joint acceleration. In this study, we provide a further investigation by proposing a new acceleration-level RMP scheme for redundant robot manipulators. By using the methods of gradient descent and neural dynamics, the proposed scheme, which is based on Jacobian pseudoinverse, is theoretically analyzed and proven to achieve the RMP of redundant robot manipulators at the joint-acceleration level. Simulation results under the UR5 robot manipulator are presented to validate the effectiveness of the proposed acceleration-level RMP scheme.