摘要
针对A^(*)算法在路径规划中存在遍历节点数过多、转折角度较大的问题,提出一种能自适应场景地图的改进A^(*)算法。通过量化地图场景信息和障碍物分布情况,引入父节点对当前节点的影响力,增加障碍物分布率的启发函数权重,减少遍历节点数量、提高搜索速度;加入转弯惩罚函数、扩展邻域优先级搜索和冗余节点平滑策略对路径进一步优化,避免路径出现多余转弯,降低路径出现局部最优解的可能。在相同地图场景中进行测试对比,所提算法能有效减少遍历节点数量,降低总转折角度,提高搜索速度,缩短路径距离,获得最优路径。
Aiming at the problems of too many traversed nodes and large turning angles in the path planning of the A^(*)algorithm,this paper proposed an improved A^(*)algorithm that could adapt to the scene map.By quantifying the map scene information and the distribution of obstacles,introducing the influence of the parent node on the current node and increasing the weight of the heuristic function of the obstacle distribution rate,this paper further reduced the number of traversed nodes and improved the search speed.By adding turning penalty function,extended neighborhood priority search and redundant node smoothing strategy further optimized the path,it avoided redundant turns on the path and reduced the possibility of local optimal solutions on the path.Finally,the simulation test in the same map scene shows that the proposed algorithm can effectively reduce the number of traversed nodes,reduce the total turning angle,improve the search speed,shorten the path distance and obtain the global optimal path.
作者
沈克宇
游志宇
刘永鑫
黄涛
Shen Keyu;You Zhiyu;Liu Yongxin;Huang Tao(Key Laboratory of Electronic Information of State Ethnic Affairs Commission,College of Electrical Engineering,Southwest Minzu University,Chengdu 610041,China)
出处
《计算机应用研究》
CSCD
北大核心
2023年第1期75-79,共5页
Application Research of Computers
基金
西南民族大学中央高校基本科研业务费专项资金资助项目(2021NYYXS42)。
