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传统A*算法在进行路径规划时存在搜索效率低、冗余点多、不能及时躲避环境中的未知障碍物等问题，现将改进A*算法和动态窗口法(Dynamic Window Approach, DWA)融合后进行机器人路径规划.首先在传统A*算法的启发函数前引入动态权重系数；然后采用关键点选取策略剔除路径上的冗余节点；最后将改进A*算法所规划的路径上的关键点作为DWA算法的中间目标点，在全局路径的基础上实现动态避障.仿真结果表明，该融合算法能在躲避动态障碍物的同时快速规划出一条全局最优路径.
Abstract：The traditional A* algorithm has problems such as low search efficiency, many redundancy points, and inability to avoid unknown obstacles in the environment in time, so the A* algorithm and Dynamic Window Approach(DWA) are integrated to carry out robot path planning. Firstly, the traditional A* algorithm is improved, and the dynamic weight coefficient is introduced before the heuristic function of the traditional A* algorithm to achieve adaptive adjustment to improve the search efficiency of the algorithm, secondly, the key point selection strategy is used to eliminate redundant nodes on the path, and a global path containing only key points is planned, and finally the key points on the path planned by the improved A* algorithm are used as the intermediate target points of the DWA algorithm, so that the local path follows the global path and realizes dynamic obstacle avoidance. The simulation results show that the comprehensive performance of the fusion algorithm is better than that of the two algorithms alone, and it can quickly plan a global optimal path while avoiding dynamic obstacles.
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基本信息:
DOI：10.13804/j.cnki.2095-6991.2024.01.011
中图分类号:TP242;TP18
引用信息:
[1]丰雪艳,李振璧.融合改进的A~*算法和动态窗口法的机器人路径规划[J].兰州文理学院学报(自然科学版),2024,38(01):50-54+65.DOI:10.13804/j.cnki.2095-6991.2024.01.011.
基金信息:
安徽省教育高校自然科学重点研究项目(KJ2019A1307); 亳州学院自然科学研究一般项目(BYZ2018C01)
2024-01-10
2024-01-10