关键词: 路径规划, 三维空间, 粒子群, 自适应, Logistic混沌映射

Abstract: An adaptive chaotic particle swarm optimization algorithm（SACPSO） is proposed for three-dimensional space path planning. Firstly, the three-dimensional space environment modeling is carried out, and considers the three evaluation functions of path length, obstacle risk degree and path smoothness to formulate the fitness function. Then a new adaptive update strategy is proposed for the three control parameters in the algorithm, so as to dynamically adjust the global exploration and local exploitation capabilities of the algorithm. Finally, when the population falls into the local extremum, the proposed adaptive logistic chaotic map is used to optimize the global optimal particle and guide the population to jump out of the local extremum point. Comparing the algorithm with other improved particle swarm optimization algorithms, the results show that the algorithm uses fewer iterations when converging to the global optimal solution, and the quality of the generated path is higher, which effectively improves the computational efficiency and reliability of particle swarm optimization used in path planning problem in three-dimensional space.

Key words: path planning, three-dimensional space, particle swarm, self-adaptive, Logistic chaotic map