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Bayesian online changepoint detection to improve transparency in human-machine interaction systems
https://doi.org/10.24517/00008081
https://doi.org/10.24517/0000808148a4c53a-356c-4a75-9ea6-b55386e48855
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
TE-PR-YAMAMOTO-S-3572.pdf (610.4 kB)
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| Item type | 学術雑誌論文 / Journal Article(1) | |||||||
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| 公開日 | 2017-10-03 | |||||||
| タイトル | ||||||||
| タイトル | Bayesian online changepoint detection to improve transparency in human-machine interaction systems | |||||||
| 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_6501 | |||||||
| 資源タイプ | journal article | |||||||
| ID登録 | ||||||||
| ID登録 | 10.24517/00008081 | |||||||
| ID登録タイプ | JaLC | |||||||
| 著者 |
Hon Fai, Lau
× Hon Fai, Lau× Yamamoto, Shigeru |
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| 著者別表示 |
山本, 茂
× 山本, 茂
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| 提供者所属 | ||||||||
| 内容記述タイプ | Other | |||||||
| 内容記述 | 金沢大学融合研究域融合科学系 / 金沢大学理工研究域電子情報学系 | |||||||
| 書誌情報 |
Proceedings of the IEEE Conference on Decision and Control 号 5717959, p. 3572-3577, 発行日 2010-01-01 |
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| ISSN | ||||||||
| 収録物識別子タイプ | ISSN | |||||||
| 収録物識別子 | 0191-2216 | |||||||
| DOI | ||||||||
| 関連タイプ | isIdenticalTo | |||||||
| 識別子タイプ | DOI | |||||||
| 関連識別子 | 10.1109/CDC.2010.5717959 | |||||||
| 出版者 | ||||||||
| 出版者 | IEEE | |||||||
| 抄録 | ||||||||
| 内容記述タイプ | Abstract | |||||||
| 内容記述 | This paper discusses a way to improve transparency in human-machine interaction systems when no force sensors are available for both the human and the machine. In most cases, position-error based control with fixed proportional-derivative (PD) controllers provides poor transparency. We resolve this issue by utilizing a gain switching method, switching them to be high or low values in response to estimated force changes at the slave environment. Since the slave-environment forces change abruptly in real time, it is difficult to set the precise value of the threshold for these gain switching decisions. Moreover, the threshold value has to be observed and tuned in advance to utilize the gain switching approach. Thus, we adopt Bayesian online changepoint detection to detect the abrupt slave environment change. This changepoint detection is based on the Bayes' theorem which is typically used in probability and statistics applications to generate the posterior distribution of unknown parameters given both data and prior distribution. We then show experimental results which demonstrate the Bayesian online changepoint detection has the ability to discriminate both free motion and hard contact. Additionally, we incorporate the online changepoint detection in our proposed gain switching controller and show the superiority of our proposed controller via experiment. ©2010 IEEE. | |||||||
| 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 | |||||||
