Robotic Arm Trajectory Planning Based on Hybrid Particle Swarm Optimization

Abstract

Aiming at the problem that the basic particle swarm algorithm is easy to fall into the local optimal solution for 6-degree-of-freedom robots under kinematic constraints, a trajectory planning method based on the hybrid particle swarm optimization is proposed and adopted. The method is based on the 3-5-3 combined segmented polynomial interpolation algorithm, with the time in the segmented interval as the optimization objective, and the particle swarm algorithm is improved by combining chaotic mapping, simulated annealing algorithm, improved inertia weights and learning factors. The simulation is carried out with the PUMA560 robotic arm as the object, and the results show that the hybrid particle swarm optimization can make the robotic arm speed and acceleration continuous and without sudden change in the movement process, and the time is shortened by about 11.2% compared with that of the basic particle swarm optimization, which verifies the feasibility and effectiveness of the algorithm.