摘要
提出了一种基于保收敛粒子群优化算法的移动机器人全局路径规划策略,为移动机器人在有限时间内找到一条避开障碍物的最短路径提供了一种解决方案.首先建立环境地图模型,将连接地图中起点和终点的路径编码成粒子,然后根据障碍物位置规划出粒子的可活动区域,在此区域内产生初始种群,使粒子在受限的区域内寻找最优路径.在搜索过程中,粒子群优化算法的加速系数和惯性权重均随迭代次数自适应调节.仿真实验表明算法可在起点与终点之间找到一条简单安全的最优路径.与其他文献所提的方法进行了对比研究,结果表明本文所提算法具有更快的搜索速度和更高的搜索质量.
A global path planning method for mobile robots based on the guaranteed convergence particle swarm optimization algorithm is presented.A solution is provided for mobile robots to find the shortest path avoiding obstacles in a limited period of time.Firstly,an environmental map is set up and a path connecting the start point and the end point is coded as a particle.Then,a particular active region for particles is mapped out according to the location of obstacles.The initial particle population is generated within this region and particles fly in the active region to search for the optimum path.In the search process,both the acceleration coefficient and inertia weight of the particle swarm optimization algorithm are self-adaptively adjusted along with iteration processes.It is proved that the algorithm can plan out a simple and safe optimum path connecting the start point and the end point by simulation experiments.Comparative studies with a recently reported method show that the proposed algorithm has advantages such as faster search speed and higher search quality.
出处
《北京科技大学学报》
EI
CAS
CSCD
北大核心
2010年第3期397-402,共6页
Journal of University of Science and Technology Beijing
基金
国家自然科学基金资助项目(No.60374032)
第36批国家留学回国人员科研启动基金资助项目
