订单驱动下基于强化学习的智能车间AGV调度  

AGV scheduling for order-driven intelligent workshop based on reinforcement learning

作　　者：卫诚琨 周俊 WEI Chengkun;ZHOU Jun(School of Mechanical and Automotive Engineering,Shanghai University of Engineering Science,Shanghai 201620,China)

机构地区：[1]上海工程技术大学机械与汽车工程学院,上海201620

出　　处：《上海工程技术大学学报》2023年第4期397-403,共7页Journal of Shanghai University of Engineering Science

摘　　要：物料搬运效率对智能车间的生产调度效率有着重要影响.物料搬运任务通常由自动导引车(Automated Guided Vehicle,AGV)执行,其具有数量多、任务需求实时变化、任务下达密集等特点.为及时、高效、准确地处理AGV搬运作业,提出基于强化学习的订单驱动下智能车间AGV调度模型,使用二级调度机制,第一级以负载均衡为目标,基于规则的调度方法对AGV进行任务分配;第二级运用强化学习深度Q网络(Deep Q-Network,DQN)算法对AGV进行单智能体下的搬运路径规划,通过减少智能体动作空间维数的方式,降低调度算法的收敛难度,并通过仿真实例验证该方法的有效性和创新性.Material transporting efficiency has an important impact on the production scheduling efficiency of the intelligent workshop.Material transporting tasks are usually executed by automated guided vehicle(AGV),which have large number of tasks,real-time changes in task demand,and intensive task issuance.In order to make the AGV workflow timely,efficient and accurate,an reinforcement-learning-based AGVs'scheduling model was established with a two-level mechanism.The first level aimes for load balancing,and assigns the tasks to AGVs in a rule-based scheduling method.The second level plans each AGV's path by a reinforcement learning deep Q-network(DQN)algorithm with single agent,which can reduce the convergence difficulty of the scheduling algorithm by reducing the dimensions of the agent's action space.The effectiveness and innovation of the method was verified through simulation examples.

关 键 词：AGV调度 路径规划 强化学习 深度Q网络 智能制造 

分 类 号：TH165[机械工程—机械制造及自动化] TP271[自动化与计算机技术—检测技术与自动化装置]