基于自适应多邻域A*算法的AGV路径规划优化与平滑OA
Optimization and Smoothing of AGV Path Planning Based on Adaptive Multi-neighborhood A* Algorithm
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.
To address the issues of slow search speed,redundant nodes,and non-smooth paths in tra-ditional A* algorithm for Automated Guided Vehicle(AGV)path planning,an improved method integra-ting adaptive multi-neighborhood search and B-spline curves is proposed.Firstly,the neighborhood ex-pansion mode is dynamically selected based on the obstacle density,and the heuristic function is dynamical-ly 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.
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东莞理工学院卓越工程师学院(创新创业学院),广东 东莞 523808东莞理工学院卓越工程师学院(创新创业学院),广东 东莞 523808东莞理工学院卓越工程师学院(创新创业学院),广东 东莞 523808东莞理工学院卓越工程师学院(创新创业学院),广东 东莞 523808东莞理工学院卓越工程师学院(创新创业学院),广东 东莞 523808东莞理工学院卓越工程师学院(创新创业学院),广东 东莞 523808
信息技术与安全科学
AGV自适应A*算法B样条曲线路径平滑
AGVadaptiveA* algorithmB-spline curvepath smoothing
《机械与电子》 2026 (3)
55-60,6
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)
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