现代制造工程 ›› 2025, Vol. 536 ›› Issue (5): 91-98.doi: 10.16731/j.cnki.1671-3133.2025.05.011

• 机器人技术 • 上一篇    下一篇

融合改进A*算法与DWA的AGV实时路径规划及避障研究

赵倩, 石宇强   

  1. 西南科技大学制造科学与工程学院,绵阳 621010
  • 收稿日期:2024-09-17 出版日期:2025-05-18 发布日期:2025-05-30
  • 通讯作者: 石宇强,博士,教授,主要研究方向为智能制造系统、制造与服务系统设计与优化。E-mail:shiyuqiang@swust.edu.cn
  • 作者简介:赵倩,硕士研究生,主要研究方向为智能制造、AGV调度。E-mail:3052706929@qq.com

Research on real time path planning and obstacle avoidance of AGV by integrating improved A* algorithm and DWA

ZHAO Qian, SHI Yuqiang   

  1. School of Manufacturing Science and Engineering,Southwest University of Science and Technology,Mianyang 621010,China
  • Received:2024-09-17 Online:2025-05-18 Published:2025-05-30

摘要: 为解决使用传统A*算法进行自动导引车(Automated Guided Vehicle,AGV)路径规划时存在的问题,如算法搜索节点多、路径存在冗余节点且不平滑,以及无法处理复杂环境下出现的随机障碍物等问题,设计了一种融合改进A*算法和动态窗口(Dynamic Window Approach,DWA)算法的 AGV实时路径规划及避障方法。首先,在传统A*算法评价函数的基础上设计了自适应评价函数,使得算法在搜索过程中根据周围环境自适应调整,从而提高算法的快速性和灵活性;其次,针对路径存在冗余节点的问题,采用Floyd算法进行双向平滑度路径优化,删除多余节点,保留关键拐点,减少转向次数,有效提高路径平滑度,充分保障了AGV运行稳定性;最后,将改进A*算法与DWA算法相结合,实现了路径规划的全局最优和动态避障的有效融合。这一综合方法不仅增强了AGV在复杂环境中的路径规划能力,还提高了避障性能,为AGV的实际应用提供了更加可靠的解决方案。

关键词: A*算法, 动态窗口算法, 融合算法, 自动导引车, 实时路径规划, 动态避障

Abstract: To solve the problems of using traditional A* algorithm for AGV path planning,such as multiple search nodes,redundant and uneven paths,and inability to handle random obstacles in complex environments,a real-time path planning and obstacle avoidance method for Automated Guided Vehicles (AGVs) was designed by integrating improved A* algorithm and Dynamic Window Approach (DWA) algonthm. Firstly,an adaptive evaluation function was designed based on the traditional A* algorithm evaluation function,allowing the algorithm to adaptively adjust according to the surrounding environment during the search process,thereby improving the speed and flexibility of the algorithm. Secondly,in response to the problem of redundant nodes in the path,the Floyd algorithm was used for bidirectional smoothness path optimization,deleting redundant nodes to retain key turning points,reducing the number of turns,effectively improving path smoothness,and fully ensuring the stability of AGV operation. Finally,the improved A* algorithm was combined with the DWA algorithm to achieve effective integration of global optimization and dynamic obstacle avoidance in path planning. This comprehensive method not only enhances the path planning ability of AGV in complex environments,but also improves obstacle avoidance performance,providing a more reliable solution for the practical application of AGV.

Key words: A* algorithm, Dynamic Window Approach (DWA) algorithm, fusion algorithm, Automated Guided Vehicle (AGV), real-time path planning, dynamic obstacle avoidance

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