摘要
针对应用于复杂仓储环境的自动导引小车(AGV)轨迹规划问题,提出了一种结合改进A^(*)算法和动态窗口算法(DWA)的混合算法,命名为IA^(*)-DW混合算法,以提高AGV在复杂仓储环境中的轨迹规划能力,保证AGV在复杂环境中安全且高效率地运行。该算法的主要原理是使用加权欧氏距离优化传统A^(*)算法的启发函数,并提出一种拐点识别算法去除路径冗余节点,改进后的A^(*)算法与传统A^(*)算法相比在完成相同任务时轨迹拐点数可减少57.14%;利用改进的A^(*)算法完成全局路径规划工作,保证规划的路径全局最优,接着结合动态窗口算法完成局部路径规划。仿真实验结果表明:IA^(*)-DW混合算法较传统算法更适合用于复杂环境的AGV路径规划。
For the problem of Automated Guided Vehicle(AGV)trajectory planning in complex storage environment,a hybrid algorithm which combined improved A^(*)algorithm with Dynamic Window Approach(DWA),named IA^(*)-DW hybrid algorithm,was proposed,to improve the trajectory planning capability in complex storage environment for AGV,making the AGV run safely and efficiently in complex environments.The main principle of the hybrid algorithm is to use a weighted Euclidean distance to optimize the heuristic function of the traditional A^(*)algorithm,and put forward an inflection point identification algorithm to remove path redundancy nodes.The improved A^(*)algorithm reduced the number of trajectory nodes by 57.14%compared with the traditional A^(*)algorithm when finished the same task.The improved A^(*)algorithm was used to complete the global path planning and ensure the path was globally optimal.Then,the DWA was used to complete the local path planning.The simulation results show that the IA^(*)-DW hybrid algorithm is more suitable for AGV path planning in complex environment than the traditional algorithm.
作者
彭斌
王力
杨思霖
PENG Bin;WANG Li;YANG Silin(School of Mechanical and Electrical Engineering,Lanzhou University of Technology,Lanzhou Ganshu 730050,China;Zhejiang Yalong Intelligent Equipment Group Company Limited,Wenzhou Zhejiang 325000,China)
出处
《计算机应用》
CSCD
北大核心
2022年第S01期347-352,共6页
journal of Computer Applications
基金
国家自然科学基金资助项目(51675254,51966009)
国家重点研发计划项目(SQ2020YFF0420989)。
关键词
轨迹规划
自动导引小车
A^(*)算法
动态窗口算法
trajectory planning
Automated Guided Vehicle(AGV)
A^(*)algorithm
Dynamic Window Approach(DWA)
