关键词: 云模型, 粒子群算法, 路径规划, 自适应参数调整

Abstract: The penalty function is used to change the constrained problem into the unconstrained problem in path planning of robots. By using the randomicity and stable tendentiousness characteristics of cloud model, an adaptive strategy for varying parameters of Particle Swarm Optimization（PSO） theory is introduced based on cloud model. So an improved Particle Swarm Optimization（PSO） algorithm is constructed and applied to path planning of robots. By adopting different inertia weight generating methods in different groups, the searching ability of the algorithm in local and overall situation is balanced effectively. And it does not only improve the convergence speed, but also maintains the diversity of the population. The feasibility and effectiveness are proved by the comparative results of the simulation experiments. And also the algorithm can be achieved simply and has fast convergence rate.

Key words: cloud model, Particle Swarm Optimization（PSO） algorithm, path planning, adaptive varying parameters