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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 strategy for mobile robots based on guaranteed convergence particle swarm optimization algorithm is proposed, which provides a solution for mobile robot to find a shortest path to avoid obstacles within a limited time.First, an environment map model is established to connect The starting and ending paths in the map are coded as particles, and then the particle’s movable area is planned according to the position of the obstacle, and the initial population is generated in this area so that the particles can find the optimal path in the restricted area.In the search process, The acceleration and inertia weights of PSO are adaptively adjusted with the number of iterations. The simulation results show that the algorithm can find a simple and secure optimal path between the start point and the end point.Compared with other methods, The results show that the proposed algorithm has faster search speed and higher search quality.