Reinforcement Learning Approach for Adaptive Negotiation-Rules Acquisition in AGV Transportation Systems
Reinforcement Learning Approach for Adaptive Negotiation-Rules Acquisition in AGV Transportation Systems
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DOI：
10.20965/jaciii.2017.p0948
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发表时间：
2017-09-01
影响因子：
0.7
通讯作者：
Tamaki, Hisashi
中科院分区：
文献类型：
--
作者：
Nagayoshi, Masato;Elderton, Simon J. H.;Tamaki, Hisashi
在本文中，我们介绍了一种自主分散的方法，指导多个自动导引车（AGV）在响应不确定的交付请求。AGV的运输路径规划期望最小化运输时间，同时防止系统中AGV之间的碰撞。在该方法中，每个AGV作为代理，通过参考静态路径信息来计算其运输路线。如果检测到潜在的冲突，由协商规则选择的两个代理之一修改其路由计划。在这里，我们提出了一个强化学习的方法来改善协商规则。然后，我们确认所提出的方法的有效性的基础上的计算实验的结果。
In this paper, we introduce an autonomous decentralized method for directing multiple automated guided vehicles (AGVs) in response to uncertain delivery requests. The transportation route plans of AGVs are expected to minimize the transportation time while preventing collisions between the AGVs in the system. In this method, each AGV as an agent computes its transportation route by referring to the static path information. If potential collisions are detected, one of the two agents chosen by a negotiation-rule modifies its route plan. Here, we propose a reinforcement learning approach for improving the negotiation-rules. Then, we confirm the effectiveness of the proposed approach based on the results of computational experiments.