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Reinforcement Learning Approach for Adaptive Negotiation-Rules Acquisition in AGV Transportation Systems
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- Nagayoshi Masato
- Niigata College of Nursing
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- Elderton Simon J. H.
- Niigata College of Nursing
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- Sakakibara Kazutoshi
- Toyama Prefectural University
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- Tamaki Hisashi
- Kobe University
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Description
<p>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.</p>
Journal
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- Journal of Advanced Computational Intelligence and Intelligent Informatics
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Journal of Advanced Computational Intelligence and Intelligent Informatics 21 (5), 948-957, 2017-09-20
Fuji Technology Press Ltd.
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Keywords
Details 詳細情報について
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- CRID
- 1390001288092326528
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- NII Article ID
- 130007520209
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- NII Book ID
- AA12042502
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- ISSN
- 18838014
- 13430130
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- NDL BIB ID
- 028510981
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- Text Lang
- en
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- Data Source
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- JaLC
- NDL Search
- Crossref
- CiNii Articles
- OpenAIRE
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- Abstract License Flag
- Disallowed