Large‐Scale Surface Shape Sensing with Learning‐Based Computational Mechanics

Proprioception, the ability to perceive one's own configuration and movement in space, enables organisms to safely and accurately interact with their environment and each other. The underlying sensory nerves that make this possible are highly dense and use sophisticated communication pathways t...

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Autores principales:	Kui Wang, Chi‐Hin Mak, Justin D. L. Ho, Zhiyu Liu, Kam‐Yim Sze, Kenneth K. Y. Wong, Kaspar Althoefer, Yunhui Liu, Toshio Fukuda, Ka-Wai Kwok
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	computational mechanics ensemble learning flexible sensors robotic proprioception surface shape sensing Computer engineering. Computer hardware TK7885-7895 Control engineering systems. Automatic machinery (General) TJ212-225
Acceso en línea:	https://doaj.org/article/d53f70f301c6452088f0e990b5a10d22
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id	oai:doaj.org-article:d53f70f301c6452088f0e990b5a10d22
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spelling	oai:doaj.org-article:d53f70f301c6452088f0e990b5a10d222021-11-23T07:58:49ZLarge‐Scale Surface Shape Sensing with Learning‐Based Computational Mechanics2640-456710.1002/aisy.202100089https://doaj.org/article/d53f70f301c6452088f0e990b5a10d222021-11-01T00:00:00Zhttps://doi.org/10.1002/aisy.202100089https://doaj.org/toc/2640-4567Proprioception, the ability to perceive one's own configuration and movement in space, enables organisms to safely and accurately interact with their environment and each other. The underlying sensory nerves that make this possible are highly dense and use sophisticated communication pathways to propagate signals from nerves in muscle, skin, and joints to the central nervous system wherein the organism can process and react to stimuli. In a step forward to realize robots with such perceptive capability, a flexible sensor framework that incorporates a novel modeling strategy, taking advantage of computational mechanics and machine learning, is proposed. The sensor framework on a large flexible sensor that transforms sparsely distributed strains into continuous surface is implemented. Finite element (FE) analysis is utilized to determine design parameters, while an FE model is built to enrich the morphological data used in the supervised training to achieve continuous surface reconstruction. A mapping between the local strain data and the enriched surface data is subsequently trained using ensemble learning. This hybrid approach enables real time, robust, and high‐order surface reconstruction. The sensing performance is evaluated in terms of accuracy, repeatability, and feasibility with numerous scenarios, which has not been demonstrated on such a large‐scale sensor before.Kui WangChi‐Hin MakJustin D. L. HoZhiyu LiuKam‐Yim SzeKenneth K. Y. WongKaspar AlthoeferYunhui LiuToshio FukudaKa-Wai KwokWileyarticlecomputational mechanicsensemble learningflexible sensorsrobotic proprioceptionsurface shape sensingComputer engineering. Computer hardwareTK7885-7895Control engineering systems. Automatic machinery (General)TJ212-225ENAdvanced Intelligent Systems, Vol 3, Iss 11, Pp n/a-n/a (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	computational mechanics ensemble learning flexible sensors robotic proprioception surface shape sensing Computer engineering. Computer hardware TK7885-7895 Control engineering systems. Automatic machinery (General) TJ212-225
spellingShingle	computational mechanics ensemble learning flexible sensors robotic proprioception surface shape sensing Computer engineering. Computer hardware TK7885-7895 Control engineering systems. Automatic machinery (General) TJ212-225 Kui Wang Chi‐Hin Mak Justin D. L. Ho Zhiyu Liu Kam‐Yim Sze Kenneth K. Y. Wong Kaspar Althoefer Yunhui Liu Toshio Fukuda Ka-Wai Kwok Large‐Scale Surface Shape Sensing with Learning‐Based Computational Mechanics
description	Proprioception, the ability to perceive one's own configuration and movement in space, enables organisms to safely and accurately interact with their environment and each other. The underlying sensory nerves that make this possible are highly dense and use sophisticated communication pathways to propagate signals from nerves in muscle, skin, and joints to the central nervous system wherein the organism can process and react to stimuli. In a step forward to realize robots with such perceptive capability, a flexible sensor framework that incorporates a novel modeling strategy, taking advantage of computational mechanics and machine learning, is proposed. The sensor framework on a large flexible sensor that transforms sparsely distributed strains into continuous surface is implemented. Finite element (FE) analysis is utilized to determine design parameters, while an FE model is built to enrich the morphological data used in the supervised training to achieve continuous surface reconstruction. A mapping between the local strain data and the enriched surface data is subsequently trained using ensemble learning. This hybrid approach enables real time, robust, and high‐order surface reconstruction. The sensing performance is evaluated in terms of accuracy, repeatability, and feasibility with numerous scenarios, which has not been demonstrated on such a large‐scale sensor before.
format	article
author	Kui Wang Chi‐Hin Mak Justin D. L. Ho Zhiyu Liu Kam‐Yim Sze Kenneth K. Y. Wong Kaspar Althoefer Yunhui Liu Toshio Fukuda Ka-Wai Kwok
author_facet	Kui Wang Chi‐Hin Mak Justin D. L. Ho Zhiyu Liu Kam‐Yim Sze Kenneth K. Y. Wong Kaspar Althoefer Yunhui Liu Toshio Fukuda Ka-Wai Kwok
author_sort	Kui Wang
title	Large‐Scale Surface Shape Sensing with Learning‐Based Computational Mechanics
title_short	Large‐Scale Surface Shape Sensing with Learning‐Based Computational Mechanics
title_full	Large‐Scale Surface Shape Sensing with Learning‐Based Computational Mechanics
title_fullStr	Large‐Scale Surface Shape Sensing with Learning‐Based Computational Mechanics
title_full_unstemmed	Large‐Scale Surface Shape Sensing with Learning‐Based Computational Mechanics
title_sort	large‐scale surface shape sensing with learning‐based computational mechanics
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/d53f70f301c6452088f0e990b5a10d22
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Large‐Scale Surface Shape Sensing with Learning‐Based Computational Mechanics

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