Robot Path Planning Based on Tabu Particle Swarm Optimization Integrating Cauchy Mutation

Abstract

In view of the shortcomings of Particle Swarm Optimization (PSO) in robot path planning, such as long path planning time, slow convergence speed, insufficient search ability in the middle and late stages, and easy to fall into local optimum. Inspired by Tabu Search (TS) algorithm and Cauchy mutation, this paper proposes a Tabu Particle Swarm Optimization (PSOTS) based on Cauchy mutation. Specifically, beta distribution random number strategy is used to adaptively adjust the inertia weight. Additionally, Cauchy mutation is introduced to disturb the particles and prevent local optimum. Finally, roulette method is used to select some particles and adopt Tabu Search (TS) algorithm. To verify the performance of PSOTS algorithm in path planning, it's challenged against PSO algorithm on 2 benchmark test instances and a grid obstacle environment, being compared with four algorithms in 8 grid obstacle environments, with the results revealing that PSOTS algorithm has a better performance and can be used in robot path planning.