Solving redundant inverse kinematics of CMOR based on chaos-driven particle swarm optimization algorithm

Abstract

The China Fusion Engineering Test Reactor (CFETR) multipurpose overload robot (CMOR) is used to maintain the vacuum chamber inner components damaged by heat loads, electromagnetic fields, and nuclear radiation. The CMOR is a redundant robot whose configuration does not meet the condition of the Pieper criterion. Redundant kinematic problems can only be solved by numerical methods, not analytical methods. Conventional numerical iterative methods include considerable computational load, accumulated errors, and the Jacobian matrix's singularity. To solve redundant inverse kinematics efficiently, we compared the behaviors of 25 versions of particle swarm optimization (PSO) algorithms with 12 one-dimensional chaotic maps under unimodal and multimodal test functions. Moreover, we selected three chaos-driven PSO algorithms with optimal convergence performance to address CMOR inverse kinematics. The experimental results indicated that chaos-driven PSO algorithms have higher computational efficiency and can effectively improve the speed and accuracy of algorithm convergence. This proposed algorithm delivers a novel and efficient method for inverse kinematics of redundant robots based on chaotic maps.