Abstract: In order to solve the inverse kinematics solving problem of the cable-driven redundant manipulator,the particle swarm algorithm was improved based on the strategies of chaotic mapping and inverse learning,and sub-population particles as well as the dynamic adjustment strategy of the population size were introduced to propose a Dynamic Reorganization Multi-swarm Particle Swarm Optimization (DRMPSO) algorithm with chaotic initialization. The kinematic model of the cable-driven redundant manipulator was established,and the fitness function with the minimum position error and the minimum energy consumption at the end of the manipulator was constructed. By comparing with other algorithms for the inverse kinematics of the manipulator,the proposed dynamic reorganization multi-swarm particle swarm algorithm has obvious advantages in both solution accuracy and stability. Finally,the dynamic reorganization multi-swarm particle swarm algorithm was used in the motion planning of the robotic arm,and the simulation results show that the dynamic reorganization multi-swarm particle swarm algorithm can ensure the continuity of the attitude while the robotic arm reaches the end position,and the robotic arm motion process is smooth.

Key words: cable-driven redundant manipulator, inverse kinematics, particle swarm optimization algorithm, chaotic mapping