基于离散时空网络的多自动引导车路径规划问题
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TP242.3

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国家自然科学基金(71761013);江西省自然科学基金(20181BAB201010)第一作者:徐翔斌(1975-),男,汉族,江西湖口人,博士,教授,研究方向:物流与供应链管理。E-mail: xuxiangbin@ecjtu.edu.cn. ,李紫阳


Path planning problem of multi-AGV based on discrete space-time network
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    摘要:

    针对多AGV(automatic guided vehicle)在仓储物流搬运系统中的巷道拥堵问题,提出一种规避拥堵的系统优化策略,将产生的AGV拣货路径作为约束生成后续AGV运行轨迹。对仓库相邻节点赋予时间链接,构建时空网络地图,在此环境建立基于离散时空网络的考虑拥堵的路径优化模型,并设计了时空网络与SA(simulated annealing)相结合的全局优化算法ST-SA(space time simulated annealing) 以求解该模型,通过仿真实验对模型及算法的有效性进行验证。实验结果表明:系统优化策略可以对AGV路径规划过程进行控制与优化,ST-SA能够很快搜索到合理、高效的AGV拣货路径方案,缩短AGV在巷道的作业时间,避免了多AGV在智能仓储系统中的碰撞及拥堵。

    Abstract:

    Aiming at the roadway congestion problem of multiple AGVs (automatic guided vehicles) in the automated storage and retrieval system, a system optimization strategy to avoid congestion was proposed, and the generated AGV picking path was used as a constraint to generate subsequent AGV running trajectories. Adjacent nodes of the warehouse was assigned time links to build a space-time network map, and a path optimization model was established based on discrete space-time networks in this environment, which considered congestion. A global optimization algorithm ST-SA(space time simulated annealing) was designed that combined space-time networks and SA (simulated annealing) to solve the model, and through simulation experiments, the effectiveness of the model and algorithm was proved. The experimental results show that the system optimization strategy can control and optimize the AGV path planning process. ST-SA can quickly search for a reasonable and efficient AGV picking path plan, reduce the AGV’s working time in the roadway and avoid multiple AGVs collision and congestion in the automated storage and retrieval system.

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徐翔斌,李紫阳. 基于离散时空网络的多自动引导车路径规划问题[J]. 科学技术与工程, 2021, 21(33): 14209-14219.
Xu Xiangbin, Li Ziyang. Path planning problem of multi-AGV based on discrete space-time network[J]. Science Technology and Engineering,2021,21(33):14209-14219.

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  • 收稿日期:2021-03-16
  • 最后修改日期:2021-08-26
  • 录用日期:2021-08-27
  • 在线发布日期: 2021-11-23
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