Vision based supervised restricted Boltzmann machine helps to actuate novel shape memory alloy accurately

Abstract Extraordinary shape recovery capabilities of shape memory alloys (SMAs) have made them a crucial building block for the development of next-generation soft robotic systems and associated cognitive robotic controllers. In this study we desired to determine whether combining video data analys...

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Autores principales:	Ritaban Dutta, Cherry Chen, David Renshaw, Daniel Liang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b949d8434bfc466ebaecf0a4f65542a4
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id	oai:doaj.org-article:b949d8434bfc466ebaecf0a4f65542a4
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spelling	oai:doaj.org-article:b949d8434bfc466ebaecf0a4f65542a42021-12-02T18:50:44ZVision based supervised restricted Boltzmann machine helps to actuate novel shape memory alloy accurately10.1038/s41598-021-95939-y2045-2322https://doaj.org/article/b949d8434bfc466ebaecf0a4f65542a42021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95939-yhttps://doaj.org/toc/2045-2322Abstract Extraordinary shape recovery capabilities of shape memory alloys (SMAs) have made them a crucial building block for the development of next-generation soft robotic systems and associated cognitive robotic controllers. In this study we desired to determine whether combining video data analysis techniques with machine learning techniques could develop a computer vision based predictive system to accurately predict force generated by the movement of a SMA body that is capable of a multi-point actuation performance. We identified that rapid video capture of the bending movements of a SMA body while undergoing external electrical excitements and adapting that characterisation using computer vision approach into a machine learning model, can accurately predict the amount of actuation force generated by the body. This is a fundamental area for achieving a superior control of the actuation of SMA bodies. We demonstrate that a supervised machine learning framework trained with Restricted Boltzmann Machine (RBM) inspired features extracted from 45,000 digital thermal infrared video frames captured during excitement of various SMA shapes, is capable to estimate and predict force and stress with 93% global accuracy with very low false negatives and high level of predictive generalisation.Ritaban DuttaCherry ChenDavid RenshawDaniel LiangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ritaban Dutta Cherry Chen David Renshaw Daniel Liang Vision based supervised restricted Boltzmann machine helps to actuate novel shape memory alloy accurately
description	Abstract Extraordinary shape recovery capabilities of shape memory alloys (SMAs) have made them a crucial building block for the development of next-generation soft robotic systems and associated cognitive robotic controllers. In this study we desired to determine whether combining video data analysis techniques with machine learning techniques could develop a computer vision based predictive system to accurately predict force generated by the movement of a SMA body that is capable of a multi-point actuation performance. We identified that rapid video capture of the bending movements of a SMA body while undergoing external electrical excitements and adapting that characterisation using computer vision approach into a machine learning model, can accurately predict the amount of actuation force generated by the body. This is a fundamental area for achieving a superior control of the actuation of SMA bodies. We demonstrate that a supervised machine learning framework trained with Restricted Boltzmann Machine (RBM) inspired features extracted from 45,000 digital thermal infrared video frames captured during excitement of various SMA shapes, is capable to estimate and predict force and stress with 93% global accuracy with very low false negatives and high level of predictive generalisation.
format	article
author	Ritaban Dutta Cherry Chen David Renshaw Daniel Liang
author_facet	Ritaban Dutta Cherry Chen David Renshaw Daniel Liang
author_sort	Ritaban Dutta
title	Vision based supervised restricted Boltzmann machine helps to actuate novel shape memory alloy accurately
title_short	Vision based supervised restricted Boltzmann machine helps to actuate novel shape memory alloy accurately
title_full	Vision based supervised restricted Boltzmann machine helps to actuate novel shape memory alloy accurately
title_fullStr	Vision based supervised restricted Boltzmann machine helps to actuate novel shape memory alloy accurately
title_full_unstemmed	Vision based supervised restricted Boltzmann machine helps to actuate novel shape memory alloy accurately
title_sort	vision based supervised restricted boltzmann machine helps to actuate novel shape memory alloy accurately
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b949d8434bfc466ebaecf0a4f65542a4
work_keys_str_mv	AT ritabandutta visionbasedsupervisedrestrictedboltzmannmachinehelpstoactuatenovelshapememoryalloyaccurately AT cherrychen visionbasedsupervisedrestrictedboltzmannmachinehelpstoactuatenovelshapememoryalloyaccurately AT davidrenshaw visionbasedsupervisedrestrictedboltzmannmachinehelpstoactuatenovelshapememoryalloyaccurately AT danielliang visionbasedsupervisedrestrictedboltzmannmachinehelpstoactuatenovelshapememoryalloyaccurately
_version_	1718377527859216384

Vision based supervised restricted Boltzmann machine helps to actuate novel shape memory alloy accurately

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