随机树与微粒群融合的机器人路径规划新算法

A Novel Path Planning for Robots Based on Rapidly-Exploring Random Tree and Particle Swarm Optimizer Algorithm

提出一种全新的随机树与微粒群融合的机器人路径规划算法;利用随机树算法全局搜索能力强,而微粒群算法局部搜索能力强的特点;先是用随机树算法进行快速搜索得到全局导航路径,然后用微粒群算法局部搜索,得到更优路径;在VC环境下实验仿真表明,算法缩短了路径长度,且具有可行性和实时性。

Based on Rapidly--exploring Random Tree （RRT） and Particle Swarm Optimizer （PSO） a novel path planning of the robot is proposed. First the Rapidly--exploring Random Tree algorithm is used to obtain the global navigation path, and the Particle Swarm Optimi- zer algorithm is adopted to get the better path. Computer experiment in VC results demonstrate that this novel algorithm can shorten the path greatly and is feasible and with real time.