2020年7月8日
A Conflict-Free Routing Method for Automated Guided Vehicles Using Reinforcement Learning
2020 International Symposium on Flexible Automation
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- 記述言語
- 英語
- 掲載種別
- 研究論文(国際会議プロシーディングス)
- DOI
- 10.1115/isfa2020-9620
- 出版者・発行元
- American Society of Mechanical Engineers
<title>Abstract</title>
In a modern large-scale fabrication, hundreds of vehicles are used for transportation. Since traffic conditions are changing rapidly, the routing of automated guided vehicles (AGV) needs to be changed according to the change in traffic conditions. We propose a conflict-free routing method for AGVs using reinforcement learning in dynamic transportation. An advantage of the proposed method is that a change in the state can be obtained as an evaluation function. Therefore, the action can be selected according to the states. A deadlock avoidance method in bidirectional transport systems is developed using reinforcement learning. The effectiveness of the proposed method is demonstrated by comparing the performance with the conventional Q learning algorithm from computational results.
In a modern large-scale fabrication, hundreds of vehicles are used for transportation. Since traffic conditions are changing rapidly, the routing of automated guided vehicles (AGV) needs to be changed according to the change in traffic conditions. We propose a conflict-free routing method for AGVs using reinforcement learning in dynamic transportation. An advantage of the proposed method is that a change in the state can be obtained as an evaluation function. Therefore, the action can be selected according to the states. A deadlock avoidance method in bidirectional transport systems is developed using reinforcement learning. The effectiveness of the proposed method is demonstrated by comparing the performance with the conventional Q learning algorithm from computational results.
- リンク情報
- ID情報
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- DOI : 10.1115/isfa2020-9620