基于改进A^(*)算法的路径规划研究

Research on Path Planning Based on Improved A^(*) Algorithm

研究了A^(*)算法在二、三维模型路径规划中的优化方法。通过实时阈值法和惩罚因子法减少开放列表中不必要的搜索空间和冗余路径;采用自定义优先级队列、二叉堆法和哈希表替代传统A^(*)算法中的处理方式;在对二维地图的研究中,采用局部A^(*)算法避免大面积搜索。实验结果表明,经过改进的A^(*)算法显著提高了搜索和路径规划速度,减少了计算时间和内存消耗,验证了该算法的可行性和有效性。

It studies the optimization method of A^(*)algorithm in path planning of 2D and 2D models.Reduce unnecessary search space and redundant paths in open lists through real-time threshold method and penalty factor method.Adopting custom priority queues,binary heap methods,and hash tables to replace the processing methods in traditional A^(*)algorithms.In the study of 2D maps,local A^(*)algorithm is used to avoid large-scale searches.The experimental results show that the improved A^(*)algorithm significantly improves the speed of search and path planning,reduces computational time and memory consumption,and verifies the feasibility and effectiveness of the algorithm.