Jixie chuandong (Feb 2021)
Inverse Kinematics Solution of Redundant Manipulator based on Improved Particle Swarm Optimization Algorithm
Abstract
Taking the minimizing pose error of the end-effector as the objective function， the inverse kinematics problem of redundant manipulator is transformed into an equivalent optimization problem. An improved particle swarm optimization algorithm is proposed to solve this optimization problem. The algorithm is comprehensively improved from the aspects of particle population initialization， inertia weight adjustment strategy， differential mutation operation as well as the boundary violation treatment of searching space. At the same time， a two-stage hybrid coevolution mechanism based on particle swarm evolution and differential mutation evolution is constructed. As a result， the global exploration and the local exploitation of the algorithm are reasonably and effectively balanced， the convergence precision and speed of the algorithm are improved. Taking the inverse kinematics solutions of a planar redundant manipulator and a 7-DOF redundant manipulator as examples， the proposed algorithm and the comparison algorithms are used to solve the inverse kinematics problem. The simulation results show that compared with the comparison algorithm， the proposed algorithm has higher convergence accuracy， faster convergence speed and stronger optimization stability. The proposed algorithm can effectively solve the inverse kinematics problem of redundant manipulator.
