摘要
针对原始A^(*)算法在AGV(automatic guided vehicle)路径规划时存在搜寻效率低、搜寻过多不必要位置,且生成路线转弯较多的缺点,提出一种目标车位搜索策略结合跳点策略的改进A^(*)算法。加入目标车位搜索策略,判断目标车位位于初始位置的象限,进行定向搜寻,再利用跳点策略删除搜寻过程中无关位置内存占用,从而提高搜索效率。引入车辆转弯成本,使生成路线的转弯次数尽可能少。通过Matlab 2022b软件在14×16车库地图、20×20车库地图以及30×30车库地图对改进A^(*)算法进行对比测试,结果表明,改进A^(*)算法相比原始A*算法,行驶路线搜索时间、搜寻位置个数以及转弯次数大幅减少,能够有效提高停车AGV工作效率,生成最佳行驶路线。
The A^(*) algorithm in AGV path planning suffers such problems as low search efficiency,unnecessary search of positions,and generation of routes with more turns.To address these problems,this paper proposes an improved A^(*) algorithm based on target position search strategy.First,the target location search strategy is added.Then,the target is located in the quadrant of the initial location for directional search in order to improve the search efficiency.Finally,the vehicle turning cost is introduced to reduce the number of turns of the parking AGV during traveling.The Dijkstra algorithm,A^(*) algorithm and improved A^(*) algorithm are compared and tested by Matlab 2022b software in the 14×16 garage map,the 20×20 random map,and the 30×30 random map.Results show the improved A*algorithm substantially cuts the driving route search time,the number of search positions,and the number of turns compared with the other two algorithms,effectively improving the efficiency of parking AGVs and generating the optimal traveling route.
作者
蒋强
杨媛斌
JIANG Qiang;YANG Yuanbin(School of Mechanical Engineering,Chongqing University of Technology,Chongqing 400054,China)
出处
《重庆理工大学学报(自然科学)》
北大核心
2025年第10期102-108,共7页
Journal of Chongqing University of Technology:Natural Science
基金
重庆市巴南区科技项目(2020TJZ014)。
作者简介
蒋强,男,博士,副教授,主要从事智慧静态交通系统与物联网技术研究,E-mail:jiangqiang@cqut.edu.cn;通信作者:杨媛斌,男,硕士研究生,主要从事智能立体车库AGV路径规划研究,E-mail:1903316310@qq.com。
