A Chaotic Adaptive Particle Swarm Optimization for Robot Path Planning

In this paper, a chaotic adaptive particle swarm optimization (CAPSO) algorithm is proposed for global path planning of mobile robots under static environment. The fitness function of CAPSO synthesizes two evaluation functions which consider path length and obstacle risk degree separately. Then the beetle antennae search algorithm (BAS) is introduced to modify the particle position updating equation to strengthen the global search ability of the algorithm. The trigonometric function is adopted for the adaptive adjustment of the control parameters for CAPSO to achieve the best cooperation in each stage of the algorithm operation and improves the local search ability of the algorithm. The chaotic map is also used to replace the random parameters of the basic particle swarm optimization. This procedure can improve the diversity of particle swarm and maintain the original random characteristics. By CAPSO the global search ability and search speed are improved and optimal robot path planning under static environment is realized. The simulation results verify the effectiveness of the proposed algorithm.