摘要
在机器人智能控制的研究中,路径规划是移动机器人研究的重要内容。为提高常规路径规划方法中执行效率和稳定性,采用头尾双向搜索法对普通A*算法进行优化,即分别从起始节点和目标节点开始扩展,直到在中途有相同的临界子节点。同时改进节点h值的计算方式,以减少扩展节点的规模,并在仿真平台上进行机器人路径规划仿真,改进算法效果得以优化验证。仿真实验结果表明,该方法的寻优能力及稳定性均优于普通A*算法,可使智能机器人更高效地进行自主导航。
Path planning is important subjects in research of mobile robots control. In order to increase the efficiency and stability of usual ways used in path planning, the searching method of double direction has been used to optimize common A ＊ algorithm. Such method may expand the starting node and the goal node at the same time, and if a same adjacency child node was found in the way, the algorithm would be terminated. In the meanwhile, the calculating way of the h value of a node was also improved to reduce the size of the extended nodes. By the simulation of path planning on the virtual platform, the results of simulating experiments prove that the ability of finding the best solution and the stability of this method are greatly improved compared with common A ＊ method,consequently the planning path of intelligent robots can be much efficient.
出处
《计算机技术与发展》
2012年第12期108-111,共4页
Computer Technology and Development
基金
国家自然科学基金(61005008
60803049)
