基于启发式算法的智能制造车间多agv调度与路径规划问题研究

Y.J. Wang, X.Q. Liu, J. Leng, J.J. Wang, Q. Meng, M. Zhou
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引用次数: 2

摘要

为了解决智能制造车间中多台AGV的调度和路径规划问题,需要同时考虑每台AGV的工件装卸和运输。提出了一种AGV分步任务调度和路径优化模型。其过程如下:首先,为优化目标建立了数学模型算法和基于工件加工紧急程度的物料运输任务分配算法,并将货架与加工设备之间的所有工件运输任务序列分配给相应的AGV,生成每个AGV的初始可行路径;然后,设计AGV碰撞检测和防碰撞算法,规划车间内多AGV的全局无碰撞行走路径,并可根据配送任务动态调整路径。采用启发式算法、蚁群算法和MATLAB编码对模型进行求解。最后通过算例验证了该方法的有效性,该方法能够有效地解决多AGV的任务分配和基于运输任务序列的避撞路径规划问题,提高AGV的工作效率。该研究可为智能化生产车间采用AGV自动化物料输送系统实现多AGV协同调度提供理论依据和实践参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Study on scheduling and path planning problems of multi-AGVs based on a heuristic algorithm in intelligent manufacturing workshop
In order to solve the scheduling and path planning problems of multi-AGVs in an intelligent manufacturing workshop, it is necessary to consider loading, unloading, and transporting the workpiece of each AGV at the same time. A step task scheduling and path optimization mode of AGV is proposed. The process is as follows: Firstly, a mathematical model algorithm and a material transportation task allocation algorithm based on the urgency degree of workpiece processing were established for the optimization objective, and all workpiece transportation task sequences between shelves and processing equipment were assigned to the corresponding AGV to generate the initial feasible path of each AGV. Then, the AGV collision detection and anti-collision algorithm are designed to plan the global collision-free walking path of multi-AGVs in the workshop, and the path can be dynamically adjusted according to the delivery task. The model is solved by a heuristic algorithm ant colony algorithm and MATLAB coding. Finally, an example is given to verify the effectiveness of the method, which can effectively solve the task allocation of multi-AGVs and avoid collision path planning based on the transportation task sequence, and improve the work efficiency of AGV. This research can provide a theoretical basis and practical reference for realizing multi AGVs collaborative scheduling by using AGV automated material transport system in an intelligent production workshop.
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