货架下方可通行无人仓中多自动导引车系统的路径规划
2024,46(4):104-113
杨洪玖
天津大学 电气自动化与信息工程学院, 天津 300072，yanghongjiu@tju.edu.cn
赵国威
天津大学 电气自动化与信息工程学院, 天津 300072
李洪波
北京极智嘉科技股份有限公司, 北京 100012
程富阳
天津大学 电气自动化与信息工程学院, 天津 300072
天津大学 电气自动化与信息工程学院, 天津 300072，yanghongjiu@tju.edu.cn
赵国威
天津大学 电气自动化与信息工程学院, 天津 300072
李洪波
北京极智嘉科技股份有限公司, 北京 100012
程富阳
天津大学 电气自动化与信息工程学院, 天津 300072
摘要：
多自动导引车 (automated guided vehicle, AGV) 系统进行搬运作业时容易发生冲突、碰撞和死锁。为此,引入多值栅格和交通规则改进A*算法解决货架下方可通行场景下多AGV系统的轨迹规划问题。为提升系统搬运效率,通过增加货架召回机制、转弯代价和热度代价对规划路径进行优化,并采用二叉堆数据结构提高路径规划速度。利用Python搭建了可视化的四方向栅格无人仓模型,对改进A*算法和轨迹优化策略进行仿真验证。仿真结果表明,改进A算法具有求解速度快、防死锁能力强的优点,可以快速完成该场景下的多AGV系统的路径规划。
多自动导引车 (automated guided vehicle, AGV) 系统进行搬运作业时容易发生冲突、碰撞和死锁。为此,引入多值栅格和交通规则改进A*算法解决货架下方可通行场景下多AGV系统的轨迹规划问题。为提升系统搬运效率,通过增加货架召回机制、转弯代价和热度代价对规划路径进行优化,并采用二叉堆数据结构提高路径规划速度。利用Python搭建了可视化的四方向栅格无人仓模型,对改进A*算法和轨迹优化策略进行仿真验证。仿真结果表明,改进A算法具有求解速度快、防死锁能力强的优点,可以快速完成该场景下的多AGV系统的路径规划。
基金项目：
国家自然科学基金资助项目(61973230)
国家自然科学基金资助项目(61973230)
Path planning of a multiple AGV system in unmanned warehouse with bottom passable shelves
YANG Hongjiu
School of Electrical Automation and Information Engineering, Tianjin University, Tianjin 300072, China,yanghongjiu@tju.edu.cn
ZHAO Guowei
School of Electrical Automation and Information Engineering, Tianjin University, Tianjin 300072, China
LI Hongbo
Geekplus Technology Co., Ltd., Beijing 100012, China
CHENG Fuyang
School of Electrical Automation and Information Engineering, Tianjin University, Tianjin 300072, China
School of Electrical Automation and Information Engineering, Tianjin University, Tianjin 300072, China,yanghongjiu@tju.edu.cn
ZHAO Guowei
School of Electrical Automation and Information Engineering, Tianjin University, Tianjin 300072, China
LI Hongbo
Geekplus Technology Co., Ltd., Beijing 100012, China
CHENG Fuyang
School of Electrical Automation and Information Engineering, Tianjin University, Tianjin 300072, China
Abstract：
The multiple AGV (automated guided vehicle) system is prone to conflict, collision and deadlock in the process of carrying. Therefore, the A* algorithm was improved by introducing multi-valued grid and traffic rules to solve the path planning problem for the multiple AGV system in scenarios where there was passable space beneath shelves. To improve handling efficiency of the multiple AGV system, the planned path was optimized by adding shelf recall mechanism, turning cost and heat cost. A binary heap data structure was also used to improve calculation speed of the path planning. The simulation results show that the improved A* algorithm has the advantages of fast solution and strong deadlock resistance, and can quickly complete the path planning of multi-AGV system in this scenario.
The multiple AGV (automated guided vehicle) system is prone to conflict, collision and deadlock in the process of carrying. Therefore, the A* algorithm was improved by introducing multi-valued grid and traffic rules to solve the path planning problem for the multiple AGV system in scenarios where there was passable space beneath shelves. To improve handling efficiency of the multiple AGV system, the planned path was optimized by adding shelf recall mechanism, turning cost and heat cost. A binary heap data structure was also used to improve calculation speed of the path planning. The simulation results show that the improved A* algorithm has the advantages of fast solution and strong deadlock resistance, and can quickly complete the path planning of multi-AGV system in this scenario.
收稿日期：
2023-03-31
2023-03-31
