Xiong, Q.; Zhang, H., and Rong, Q., 2020. Path planning based on improved particle swarm optimization for AUVs. In: Liu, X. and Zhao, L. (eds.), Today's Modern Coastal Society: Technical and Sociological Aspects of Coastal Research. Journal of Coastal Research, Special Issue No. 111, pp. 279–282. Coconut Creek (Florida), ISSN 0749-0208.
Path planning plays an important role in the motion of autonomous underwater vehicles (AUVs). It has always been a hot research topic. Aiming at the problems of traditional particle swarm optimization (PSO) in path planning, an improved PSO algorithm is proposed in this paper. In the initialization stage, the particles are initialized by a chaotic sequence. During algorithm processing, the sine function is used to dynamically decrease the particle weight. In the region near the optimal solution selected in each iteration of the PSO algorithm, a chaos algorithm is used to avoid falling into the local optimal value. Simulation experiment results show that the improved PSO algorithm greatly increases the effectiveness of the initial particles and can get the optimal path more quickly and accurately.