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Acquisition of Self-Recovery Actions for Leg and Wheeled Autonomous Rover by Reinforcement Learning
http://hdl.handle.net/10445/8452
http://hdl.handle.net/10445/8452fd2d19e9-bd86-4783-a53d-726fb9c72a16
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
2011-09-30-ISIS2011-Horikawa-315p.pdf (872.4 kB)
Download is available from 2999/12/31.
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| Item type | 会議発表論文 / Conference Paper(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2017-03-24 | |||||
| タイトル | ||||||
| タイトル | Acquisition of Self-Recovery Actions for Leg and Wheeled Autonomous Rover by Reinforcement Learning | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_5794 | |||||
| 資源タイプ | conference paper | |||||
| 著者 |
Horikawa, Masatoshi
× Horikawa, Masatoshi× Wakahara, Takumi× Ikeda, Kazunori× 三上, 貞芳 |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | This paper proposes an adaptation method for an autonomous leg-and-wheeled robot to enable stuck-fee movement under a variety of terrains. Targeting hardware is one of the anticipated configurations for exploration tasks, which has 6 legs and a motor-driven wheel on top of each leg. The method is based on an on-line combinatorial search for both gait and wheel movement according to a performance measurement. The problem is its huge search space, and the proposing system cuts down state and action space to minimal amounts. Experiments were conducted by using physics simulation, and the results show that the proposed system could gain smooth gait/wheel control under simulated rugged ground for a variety of learning coefficients. | |||||
| 書誌情報 |
12th International Symposium on Advanced Intelligent Systems p. 315-318, 発行日 2011-09-30 |
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| 査読有無 | ||||||
| 値 | あり/yes | |||||
| 研究業績種別 | ||||||
| 値 | 国際会議/International Conference | |||||
| 単著共著 | ||||||
| 値 | 共著/joint | |||||
