Path Planning for Mobile Robots Using an Improved Reinforcement Learning Scheme
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- Kurozumi Ryota
- Hiroshima University
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- Fujisawa Shoichiro
- Takamatsu National College of Thechnology
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- Yamamoto Toru
- Hiroshima University
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- Suita Yoshikazu
- Takamatsu National College of Thechnology
説明
The current method for establishing travel routes provides modeled environmental information. However, it is difficult to create an environment model for the environments in which mobile robot travel because the environment changes constantly due to the existence of moving objects, including pedestrians. In this study, we propose a path planning system for mobile robots using reinforcement-learning systems and Cerebellar Model Articulation Controllers (CMACs). We selected the best travel route utilizing these reinforcement-learning systems. When a CMAC learns the value function of Q-Learning, it improves learning speed by utilizing the generalizing action. CMACs enable us to reduce the time needed to select the best travel route. Using simulation and real robots, we performed a path-planning experiment. We report the results of simulation and experiment on travelling by on-line learning.
収録刊行物
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- SICE Annual Conference Program and Abstracts
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SICE Annual Conference Program and Abstracts 2002 (0), 481-481, 2002
公益社団法人 計測自動制御学会
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詳細情報 詳細情報について
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- CRID
- 1390282680562270464
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- NII論文ID
- 130006960450
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可
