基于改进A^(*)算法的AGV路径规划  

AGV path planning based on improved A^(*)algorithm

作　　者：李圣杰 蒋洪伟[1] LI Shengjie;JIANG Hongwei(School of Management Science and Engineering,Beijing Information Science&Technology University,Beijing 100080,China)

机构地区：[1]北京信息科技大学管理科学与工程学院,北京100080

出　　处：《邵阳学院学报(自然科学版)》2025年第2期19-26,共8页Journal of Shaoyang University:Natural Science Edition

基　　金：北京市教委科技一般项目(KM202111232017);北京信息科技大学“青年骨干教师”支持计划(YBT202439)。

摘　　要：针对智能制造中移动机器人的路径规划问题,提出一种改进算法来解决传统A^(*)算法中存在节点冗余、计算时间长、路径转折多和实时性不足等问题。算法采取5邻域搜索规则,引入地图障碍率,根据地图特点灵活调整搜索策略,识别并删除初始路径中的冗余节点。MATLAB仿真实验表明,改进A^(*)算法在两种不同分辨率和复杂程度的地图中,计算时间缩短27%以上,路径长度缩短5%以上,节点数量减少超过71%,除此之外,路径转折次数在分辨率为30×30的地图中减少了50%,在分辨率为20×20的地图中也减少了10%,性能明显优于传统A^(*)算法。In order to enhance the path planning efficiency for mobile robots in intelligent manufacturing,an improved algorithm to solve the problems faced by the traditional A^(*)algorithm was proposed,including excessive traversal nodes,prolonged calculation time,frequent turning points,and poor real-time performance.The proposed algorithm employed a 5-neighborhood search rule and integrated a map obstacle rate,allowing adaptive strategy adjustments based on map characteristics and identifying and eliminating redundant nodes within the initial path.The MATLAB simulation results demonstrated notable enhancements of the improved A^(*)algorithm with a search speed increase exceeding 27%and a path length reduction of over 5%across two maps of varied resolution and complexity.In addition,it could reduce the number of nodes by more than 71%,and the number of path turns by 50%in a 30×30 resolution map and 10%in a 20×20 resolution map,showing clear superiority over the traditional A^(*)algorithm.

关 键 词：A^(*)算法 路径规划 栅格地图 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]