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A Study of Effective Prediction Methods of the State-Action Pair for Robot Control Using Online SVR
http://hdl.handle.net/10258/00010493
http://hdl.handle.net/10258/00010493b486eb1f-b133-47a4-989f-c5dc40b2db24
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
JRM_27_5_469 (1.1 MB)
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| Item type | 学術雑誌論文 / Journal Article.(1) | |||||||||||
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| 公開日 | 2022-03-29 | |||||||||||
| 書誌情報 |
en : Journal of Robotics and Mechatronics 巻 27, 号 5, p. 469-479, 発行日 2015 |
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| タイトル | ||||||||||||
| タイトル | A Study of Effective Prediction Methods of the State-Action Pair for Robot Control Using Online SVR | |||||||||||
| 言語 | en | |||||||||||
| 言語 | ||||||||||||
| 言語 | eng | |||||||||||
| キーワード | ||||||||||||
| 言語 | en | |||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | online state prediction | |||||||||||
| キーワード | ||||||||||||
| 言語 | en | |||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | internal state space | |||||||||||
| キーワード | ||||||||||||
| 言語 | en | |||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | learning using combination of state space and action | |||||||||||
| キーワード | ||||||||||||
| 言語 | en | |||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | prediction using combination of state space and action | |||||||||||
| キーワード | ||||||||||||
| 言語 | en | |||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | mobile robot | |||||||||||
| キーワード | ||||||||||||
| 主題Scheme | Other | |||||||||||
| 資源タイプ | ||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||
| 資源タイプ | journal article | |||||||||||
| アクセス権 | ||||||||||||
| アクセス権 | open access | |||||||||||
| アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||||||||
| 著者 |
Sugimoto, Masashi
× Sugimoto, Masashi× 倉重, 健太郎
WEKO
4695
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| 室蘭工業大学研究者データベースへのリンク | ||||||||||||
| 表示名 | 倉重 健太郎（KURASHIGE Kentarou） | |||||||||||
| URL | http://rdsoran.muroran-it.ac.jp/html/100000178_ja.html | |||||||||||
| 抄録 | ||||||||||||
| 内容記述タイプ | Abstract | |||||||||||
| 内容記述 | In order to work effectively, a robot should be able to adapt to different environments by deciding its correct course of action according to the situation, using determinants other than pre-registered commands. For this purpose, the ability to predict the future state of a robot would be effective. On the other hand, the future state of a robot varies infinitely if it depends on its current action. Therefore, it is difficult to predict only the future state. Thus, it is important to simultaneously predict the state and the action that the robot will adopt. The purpose of this study was to investigate the prediction of the advanced future state and action of a robot. In this paper, the results of the study are reported and methods that allow a robot to decide its appropriate behavior quickly, according to the predicted future state are discussed. As an application example for evaluating the proposed method, the inverted pendulum model is used and the prediction results are compared with the robot’s actual responses. Then, two methods will be discussed for predicting the robot’s state and action. To perform state and action prediction, two methods are used, firstly the Online SVR (Support Vector Regression) and secondly Online SVR and the LQR (Linear Quadratic Regulator) | |||||||||||
| 言語 | en | |||||||||||
| 出版者 | ||||||||||||
| 出版者 | Fuji Technology Press Ltd | |||||||||||
| 言語 | en | |||||||||||
| 出版者版へのリンク | ||||||||||||
| 表示名 | 10.20965/jrm.2015.p0469 | |||||||||||
| URL | https://doi.org/10.20965/jrm.2015.p0469 | |||||||||||
| DOI | ||||||||||||
| 関連タイプ | isIdenticalTo | |||||||||||
| 識別子タイプ | DOI | |||||||||||
| 関連識別子 | 10.20965/jrm.2015.p0469 | |||||||||||
| 日本十進分類法 | ||||||||||||
| 主題Scheme | NDC | |||||||||||
| 主題 | 007 | |||||||||||
| ISSN | ||||||||||||
| 収録物識別子タイプ | PISSN | |||||||||||
| 収録物識別子 | 0915-3942 | |||||||||||
| 権利 | ||||||||||||
| 言語 | en | |||||||||||
| 権利情報 | https://creativecommons.org/licenses/by/4.0/ | |||||||||||
| 著者版フラグ | ||||||||||||
| 出版タイプ | VoR | |||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||
| フォーマット | ||||||||||||
| 内容記述タイプ | Other | |||||||||||
| 内容記述 | application/pdf | |||||||||||
