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Reduced-order modeling of soft robots

We present a general strategy for the modeling and simulation-based control of soft robots. Although the presented methodology is completely general, we restrict ourselves to the analysis of a model robot made of hyperelastic materials and actuated by cables or tendons. To comply with the stringent...

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Detalles Bibliográficos
Autores principales: Chenevier, Jean, González, David, Aguado, J. Vicente, Chinesta, Francisco, Cueto, Elías
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5823373/
https://www.ncbi.nlm.nih.gov/pubmed/29470496
http://dx.doi.org/10.1371/journal.pone.0192052
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author Chenevier, Jean
González, David
Aguado, J. Vicente
Chinesta, Francisco
Cueto, Elías
author_facet Chenevier, Jean
González, David
Aguado, J. Vicente
Chinesta, Francisco
Cueto, Elías
author_sort Chenevier, Jean
collection PubMed
description We present a general strategy for the modeling and simulation-based control of soft robots. Although the presented methodology is completely general, we restrict ourselves to the analysis of a model robot made of hyperelastic materials and actuated by cables or tendons. To comply with the stringent real-time constraints imposed by control algorithms, a reduced-order modeling strategy is proposed that allows to minimize the amount of online CPU cost. Instead, an offline training procedure is proposed that allows to determine a sort of response surface that characterizes the response of the robot. Contrarily to existing strategies, the proposed methodology allows for a fully non-linear modeling of the soft material in a hyperelastic setting as well as a fully non-linear kinematic description of the movement without any restriction nor simplifying assumption. Examples of different configurations of the robot were analyzed that show the appeal of the method.
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spelling pubmed-58233732018-03-15 Reduced-order modeling of soft robots Chenevier, Jean González, David Aguado, J. Vicente Chinesta, Francisco Cueto, Elías PLoS One Research Article We present a general strategy for the modeling and simulation-based control of soft robots. Although the presented methodology is completely general, we restrict ourselves to the analysis of a model robot made of hyperelastic materials and actuated by cables or tendons. To comply with the stringent real-time constraints imposed by control algorithms, a reduced-order modeling strategy is proposed that allows to minimize the amount of online CPU cost. Instead, an offline training procedure is proposed that allows to determine a sort of response surface that characterizes the response of the robot. Contrarily to existing strategies, the proposed methodology allows for a fully non-linear modeling of the soft material in a hyperelastic setting as well as a fully non-linear kinematic description of the movement without any restriction nor simplifying assumption. Examples of different configurations of the robot were analyzed that show the appeal of the method. Public Library of Science 2018-02-22 /pmc/articles/PMC5823373/ /pubmed/29470496 http://dx.doi.org/10.1371/journal.pone.0192052 Text en © 2018 Chenevier et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Chenevier, Jean
González, David
Aguado, J. Vicente
Chinesta, Francisco
Cueto, Elías
Reduced-order modeling of soft robots
title Reduced-order modeling of soft robots
title_full Reduced-order modeling of soft robots
title_fullStr Reduced-order modeling of soft robots
title_full_unstemmed Reduced-order modeling of soft robots
title_short Reduced-order modeling of soft robots
title_sort reduced-order modeling of soft robots
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5823373/
https://www.ncbi.nlm.nih.gov/pubmed/29470496
http://dx.doi.org/10.1371/journal.pone.0192052
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