| 摘要: |
| 目的 分布式自动导引车(AGV)系统具有可扩展性、高可靠性的优势，是建设智能制造车间、实现车间智能物流的重要发展趋势，研究旨在解决分布式AGV任务分配(DAGVTA)这一影响智能制造车间资源利用和生产成本的基础关键问题。方法 引入深度强化学习思想，将各AGV视为独立的智能体，基于多智能体强化学习方法独立深度Q网络(IDQN)算法进行求解。首先，将DAGVTA问题转化为强化学习对应的部分可观测马尔可夫决策过程，将各AGV观测的车间环境状态作为神经网络的输入，通过神经网络拟合值函数输出各AGV的动作选择，并以作业任务的搬运距离作为优化目标设计相应的奖励函数。在IDQN算法架构上训练各智能体，环境中各智能体彼此独立，依靠局部观测信息决策动作。结果 开展实验研究，在不同问题规模场景下与基于规则的任务分配算法、基于市场机制的竞拍算法进行DAGVTA求解效果对比，验证所提模型和求解方法的可行性。结论 基于IDQN架构训练后的AGV智能体，在没有集中规划器的情况下具有一定的自主协同能力，能够有效协同AGV完成全部搬运任务。 |
| 关键词: 智能制造车间 分布式AGV 任务分配 深度强化学习 独立深度Q网络 |
| DOI：10.19554/j.cnki.1001-3563.2025.07.017 |
| 分类号: |
| 基金项目:国家自然科学基金(U1704156);河南省科技研发计划联合基金(242103810064);河南省高等学校重点科研资助项目(23A460003);河南省超硬磨料磨削装备重点实验室开放课题资助项目(JDKFJJ2022012) |
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| Distributed AGV Task Allocation in Intelligent Manufacturing Workshops |
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ZHANG Zhongwei1, GAO Zeng'en1, WANG Jingrui1, ZHAO Binbin1, WU Zhaoyun1, LI Peng2
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(1. Henan Key Laboratory of Superhard Abrasives and Grinding Equipment, School of Mechanical & Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China;2. Zhengzhou Deli Automation Logistics Equipment Manufacturing Co., Ltd., Zhengzhou452470, China)
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| Abstract: |
| With the advantages of scalability and high reliability, the distributed automated guided vehicle (AGV) system has become a significant development trend for building intelligent manufacturing workshops and achieving intelligent logistics in the workshops. The work aims to clarify the distributed AGV task allocation (DAGVTA) in intelligent manufacturing workshops, as a fundamental key issue, that affects resource utilization and production costs. Correspondingly, deep reinforcement learning was introduced, each AGV was regarded as an independent agent, and a multi-agent reinforcement learning method independent deep Q-network (IDQN) was utilized for solution. Firstly, the DAGVTA problem was transformed into a partially observable Markov decision process related to reinforcement learning. The workshop environment states observed by each AGV were used as inputs to the neural network, and the neural network was fitted with a value function to output the action selection for each AGV. Meanwhile, the reward function was designed with the transport distance of material handling tasks as the optimization objective. Furthermore, various agents were trained on the IDQN architecture, in which each agent in the environment was independent, and took actions based on local observation information. Finally, the experimental study in scenarios with different problem scales was conducted to verify the feasibility of the proposed model and method by comparing their solution effects with rule-based task allocation algorithms and the market-based bidding algorithm. After training, the AGV agent has a certain degree of autonomous collaboration ability and can collaborate to complete all transportation tasks without a centralized planner. |
| Key words: intelligent manufacturing workshop distributed AGV task allocation deep reinforcement learning independent deep Q-network |
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