[1]黄立标,庄嘉颖,陈宇轩,等. 基于自适应多邻域A* 算法的AGV 路径规划优化与平滑[J].机械与电子,2026,44(03):55-60.
　HUANG Libiao,ZHUANG Jiaying,CHEN Yuxuan,et al. Optimization and Smoothing of AGV Path Planning Based on Adaptive Multi-neighborhood A* Algorithm[J].Machinery & Electronics,2026,44(03):55-60.
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 基于自适应多邻域A* 算法的AGV 路径规划优化与平滑()
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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
44
期数:
2026年03期
页码:
55-60
栏目:
智能制造
出版日期:
2026-03-25

文章信息/Info

Title:
 Optimization and Smoothing of AGV Path Planning Based on Adaptive Multi-neighborhood A* Algorithm
文章编号:
1001-2257(2026)03-0055-06
作者:
 黄立标; 庄嘉颖; 陈宇轩; 张淑慧; 黄瑞金; 王福杰; 樊开夫
 (东莞理工学院卓越工程师学院(创新创业学院),广东 东莞 523808)
Author(s):
 HUANG Libiao; ZHUANG Jiaying; CHEN Yuxuan; ZHANG Shuhui; HUANG Ruijin; WANG Fujie; FAN Kaifu
 (Elite Engineers College(Innovation Entrepreneurship College),Dongguan University of Technology,Dongguan 523808,China)
关键词:
 ; AGV; 自适应; A* 算法; B样条曲线; 路径平滑
Keywords:
 ; AGV; adaptive; A* algorithm; B spline curve; path smoothing
分类号:
TP18
文献标志码:
A
摘要:
 针对传统A* 算法在自动导引车AGV 路径规划中搜索缓慢、节点冗余及路径不平滑的问题,提出一种融合自适应多邻域搜索与B样条曲线的改进方法。首先依据障碍物密度动态选择邻域扩展方式并调整启发函数,以提升搜索效率与路径质量,然后利用B样条曲线对路径进行平滑处理,确保曲率连续以满足底盘运动学约束。实验结果表明,相较于传统8 邻域A* ,所提方法路径长度缩短约16%,扩展节点减少约85%,计算时间下降约15%,转折次数与最大曲率分别降低约66.70%与62.40%,显著提高了AGV 路径的平滑度、可执行性与规划效率。
Abstract:
 To address the issues of slow search speed,redundant nodes,and non smooth paths in traditional A* algorithm for Automated Guided Vehicle (AGV) path planning,an improved method integrating adaptive multi neighborhood search and B spline curves is proposed.Firstly,the neighborhood expansion mode is dynamically selected based on the obstacle density,and the heuristic function is dynamically adjusted to enhance search efficiency and path quality.Subsequently,B spline curves are utilized to smooth the generated path,ensuring curvature continuity to meet the kinematic constraints of the chassis.Experimental results show that compared to the traditional 8 neighborhood A* algorithm,the proposed method reduces the path length by approximately 16%,decreases the number of expanded nodes by about 85%,shortens the computation time by around 15%,and reduces the number of turns and the maximum curvature by approximately 66.70% and 62.40% respectively.These improvements significantly enhance the smoothness,executability,and planning efficiency of AGV path.

参考文献/References:

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备注/Memo

备注/Memo:
 收稿日期:2025-10-17
基金项目:国家自然科学基金资助项目(62203116);广东省教育厅普通高校重点科研平台和项目(2025ZDZX3037);广东省科协青年科技人才培育计划项目(SKXRC2025441)
作者简介:黄立标 (2003-),男,广东肇庆人,研究方向为路径规划;王福杰 (1991-),男,广东广州人,博士,副教授,硕士研究生导师,研究方向为智能机器人系统与技术,通信作者,E-mail:fjwang@dgut.edu.cn。
更新日期/Last Update: 2026-04-29