针对移动机器人全局最优路径规划问题,提出一种基于细胞自动机的路径规划算法.该算法首先将移动机器人的起点、目标点和空间障碍物定义为一组离散的细胞状态,建立环境的细胞自动机模型;然后由机器人移动的曼哈顿距离设计演化规则;最后根据演化后的细胞状态搜索最优路径.对简单和复杂环境下的机器人路径规划问题进行了仿真实验,实验结果验证了该算法的有效性. In order to solve the problem of global optimal path planning for mobile robots, a cellular automata-based path planning algorithm is proposed. The algorithm first defines the starting point, target point and space obstacle of mobile robots as a group of discrete cell states, Automaton model and then the Manhattan distance design and evolution rule by the robot movement is proposed and finally the optimal path is searched based on the evolved cell states.The robot path planning problem under simple and complex environment is simulated and the experimental results verify that the algorithm Effectiveness.