Abstract: A novel path planning for robots based on adaptive chaos mutation operator particle swarm optimization algorithm is presented. The first step is to make a new map. The improved particle swarm optimization algorithm is introduced to get a global optimized path. The algorithm takes advantage of adaptive chaos mutation operator to enhance the local search ability and keeps the swarm diversity. The result of simulation shows that this novel algorithm can plan an optimal path rapidly in a cluttered environment. The successful obstacle avoidance is achieved.

Key words: path planning, particle swarm algorithm, chaos mutation, adaptive

摘要： 研究了一种全新的基于自适应混沌变异粒子群的路径规划算法。该方法首先进行环境建模，利用改进的粒子群算法获得一条较优路径。在改进的粒子算法中为防止早收敛，加入自适应混沌变异操作，在加强算法局部搜索能力的同时保证搜索过程中种群的多样性。仿真实验表明，即使在复杂的环境下，利用该算法也可以规划出一条全局较优路径，且能安全避碰。

关键词: 路径规划, 粒子群算法, 混沌变异, 自适应