Multi-Objective Path Planning Based on Parallel Elite Particle Swarm Optimization Algorithm

The task mode of robots is mostly one machine corresponding to multiple targets, which is equivalent to a multi-point path planning problem under specific constraints. To solve this problem, a parallel elite particle swarm optimization (PSO) algorithm is proposed. Firstly, the elite selection mechanism is applied to PSO algorithm, which avoids the blindness of the algorithm and improves the convergence speed. At the same time, the improved artificial potential field gravity is combined with inertia factor in the PSO algorithm to construct an adaptive update strategy of particle position and velocity. This strategy enhances the global search ability of the PSO algorithm in the early stage of robot motion, and the local search ability of the PSO algorithm gradually increases with the decrease of gravity in the later stage, which solves the problem that the PSO algorithm is easy to fall into local optimal. Secondly, with the help of the adaptive elite PSO algorithm, the safety distance between any two target points is calculated to form the corresponding safety distance matrix. Then, the PSO algorithm is used to find the optimal order according to the safety distance matrix. Finally, multi-objective point path planning is implemented according to the optimal order, and the concept of parallel optimization is introduced to improve the operating efficiency of the program algorithm. The simulation results show that the algorithm is feasible and effective.