计算机科学

计算机科学 ›› 2025, Vol. 52 ›› Issue (11A): 240900148-7.doi: 10.11896/jsjkx.240900148

• 人工智能 • 上一篇    下一篇

融合改进A*算法和TEB算法的AGV路径规划

彭可, 刘宏胜, 张志成, 朱亮, 贺劢勍, 张旭辉, 曾启瑾, 张嗣愿   

  1. 湖南师范大学工程与设计学院 长沙 410081
  • 出版日期:2025-11-15 发布日期:2025-11-10
  • 通讯作者: 彭可(77547113@qq.com)
  • 基金资助:
    国家自然科学基金(52005179);湖南省研究生科研创新项目资助(CX20220504);国家级大学生创新创业训练计划支持项目(202410542067)

Path Planning for AGV Integrating Improved A* Algorithm and TEB Algorithm

PENG Ke, LIU Hongsheng, ZHANG Zhicheng, ZHU Liang, HE Maiqing, ZHANG Xuhui, ZENG Qijin, ZHANG Siyuan   

  1. School of Engineering and Design,Hunan Normal University,Changsha 410081,China
  • Online:2025-11-15 Published:2025-11-10
  • Supported by:
    National Natural Science Foundation of China(52005179),Postgraduate Scientific Research Innovation Project of Hunan Province(CX20220504) and National College Students’ Platform for Innovation and Entrepreneurship Training Program(202410542067).

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摘要： 为提高AGV的自主导航和自主避障性能,针对A*算法路径规划中存在的长度非最短、拐点多、易碰撞等问题,提出了一种将改进后的A*算法与TEB算法相融合的AGV路径规划方法。首先将搜索领域根据一定的规则增加至12个,扩宽了AGV的搜索视野并使搜索更具导向性;接着在启发函数中添增障碍物因子,使启发函数能够根据地图中障碍物的分布情况自适应改变,有效减少了函数预估误差;最后将改进A*算法规划的全局最优路径分解为全局领航点,在两领航点间采用TEB算法进行局部路径规划,保证AGV在全局最优路径的基础上对动态障碍物进行实时规避。仿真表明,改进A*算法能够显著减少路径的拐点数、长度和节点数。另外,利用全向麦轮底盘搭建AGV实验平台,搭载融合算法进行自主导航和自主避障测验,结果表明,所提算法能够有效缩短AGV的路径长度及行驶时间,且能够安全抵达目标点,验证了其优越性。

关键词: AGV, 路径规划, A*算法, TEB算法, 融合算法

Abstract: To enhance the autonomous navigation and obstacle avoidance capabilities of Automated Guided Vehicles(AGV),this study addresses the issues of poor path smoothness,non-optimal path length,and collision susceptibility inherent in the A* algorithm.We propose an AGV path planning method that integrates an improved A* algorithm with the Timed Elastic Band(TEB) algorithm.Initially,the search domain is expanded to 12 directions based on certain rules,broadening the AGV’s search horizon and making the search more directional.Nextly,by incorporating an obstacle factor into the heuristic function,the function can adaptively change according to the distribution of obstacles on the map,effectively reducing estimation errors.Finally,the globally optimal path planned by the improved A* algorithm is decomposed into global waypoints.Between these waypoints,the TEB algorithm is used for local path planning,ensuring that the AGV can dynamically avoid obstacles in real-time while following the globally optimal path.Simulations demonstrate that the improved A* algorithm significantly reduces the number of turns,path length,and nodes.An AGV experimental platform with an omnidirectional Mecanum wheel chassis was then constructed to test the integrated algorithm’s performance in autonomous navigation and obstacle avoidance.The results show that the proposed algorithm can effectively reduce the path length and travel time of AGV,ensuring safe arrival at the target point,thereby validating its superiority.

Key words: AGV, Path planning, A* algorithm, TEB algorithm, Fusion algorithm

中图分类号: 

  • TP242
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