基于偏好粒子群算法的移动机器人路径规划

针对粒子群算法收敛速度快,但易陷入局部最优的问题,提出一种基于偏好粒子群算法的移动机器人路径规划方法.在对障碍物环境建模的基础上,根据避障偏好利用粒子群优化算法规划出全局最优路径.为避免搜索过程中算法陷入局部最优,采用深度优先搜索策略,保持了种群的多样性,提高了算法的搜索能力.实验结果表明:该方法能够有效地避开障碍物,并且获得较好的路径规划效果.

Path Planning for Mobile Robot Based on Preference Particle Swarm Optimization Algorithm

A path planning algorithm based on the preference particle swarm optimization （PSO） algorithm was proposed avoid local optimum due to the algorithm＇s fast convergence. Based on the obstacle environment modeling, PSO algorithm was used to plan out the global optimal. In order to avoid algorithm falling into local optimum, the depth-first search strategy was used in the process,which can ensure the diversity of population and improve the search ability of the algorithm. The experiment result shows that the method can effectively avoid obstacle and achieves better effect of path planning.