Kinematic Optimization of Redundant Manipulators to Improve the Fault-Tolerant Control

Abstract

To improve the fault-tolerant control of redundant manipulators, a kinematic optimization algorithm is proposed with an index based on reduced manipulability. Firstly, augmented Jacobian is derived with an arm angle to solve the inverse kinematics of redundant manipulator. The arm angle is defined to parameterize the redundancy of redundant manipulators. By changing the arm angle, different inverse kinematic solutions are obtained without changing the end-effector position and it is called self-motion. The self-motion is used for kinematic optimization. The reduced manipulability is relative to kinematic solutions. With different arm angle, the reduced manipulability is different. Thus, the kinematic optimization is accomplished to maximize the reduced manipulability by choosing the proper arm angle. The kinematic optimization algorithm is verified with Yaskawa VA1400II manipulator. The results of simulations and experiment strongly prove the validity and efficiency of this algorithm.