基于工业物联网的混流车间机器人自适应调度

随着工业物联网技术与人工智能技术深度融合,物料机器人已广泛应用于物联网车间中。针对车间存在实时动态变化和状况不确定等诸多复杂因素,本文提出以组分层建树和以实时状态为根节点的SP–MCTS(Singleplayermonte-carlotreesearchalgorithm)搜索方法实现车间自适应调度决策。该方法将机器人调度问题转化为马尔科夫决策过程(Markov decision process,MDP),并详细描述车间状态、动作、奖励和策略的表示方法。在实时调度过程中,该搜索方法依据工件组分层建树,搜索中只考虑相邻两组间的状态关系,从而简化计算难度。在子树搜索中,应用SP–MCTS以实时状态为根节点进行搜索,同时应用扩展方法和剪支方法进行策略探索和信息累积,使得在子树内实时状态节点越深,就越能够快速精准获取最优策略。最后,通过实际案例模拟分析,验证了该方法的有效性和优越性。

Adaptive Robot Scheduling Using SP-MCTS for Industrial Internet of Things-Enabled Hybrid Flow Shop

With the deep integration of industrial internet of things(IIoT)and artificial intelligence(AI)technology,automated guided vehicles(AGVs)and mobile robots have been widely used in internet of things-enabled floor shop.In view of many complex factors such as real-time dynamic changes and uncertain conditions in the workshop,the SP–MCTS(Single-player Monte-Carlo tree search algorithm)method with each job group as a subtree and real-time state as the root node is proposed to implement adaptive scheduling of workshop.The problem of robot scheduling is formulated as a Markov decision process(MDP)in which state representation,action representation,reward function,and optimal policy,are described in detail.In the real-time scheduling process,the proposed search method establishes a subtree for each job group,and only the state relationship between two adjacent groups is considered in optimization,thereby the calculation difficulty is simplified.In the subtree search process,SP–MCTS is used to search with the real-time state as the root node.At the same time,the expansion method and the pruning method are used to carry out strategy exploration and information accumulation respectively.Therefore,the deeper the real-time status node in the subtree,the faster and more accurate the optimal strategy is obtained.The case study based on a real-world shop is proven and the results validate the effectiveness and superiority of the proposed approach.