Predicting soft robot’s locomotion fitness

Detalhes bibliográficos
Autor(a) principal: Biazzi, Renata Biaggi
Data de Publicação: 2021
Outros Autores: Fujita, André, Takahashi, Daniel Yasumasa
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UFRN
Texto Completo: https://repositorio.ufrn.br/handle/123456789/33049
Resumo: Organisms with different body morphology and movement dynamics have distinct abilities to move through the environment. Despite such truism, there is a lack of general principles that predict which shapes and dynamics make the organisms more fit to move. Studying a minimal yet embodied soft robot model under the influence of gravity, we find three features that predict robot locomotion fitness: (1) A larger body is better. (2) Two-point contact with the ground is better than one-point contact. (3) Out-of-phase oscillating body parts increase locomotion fitness. These design principles can guide the selection rules for evolutionary algorithms to obtain robots with higher locomotion fitness
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spelling Biazzi, Renata BiaggiFujita, AndréTakahashi, Daniel Yasumasa2021-08-09T12:01:01Z2021-08-09T12:01:01Z2021-07-07BIAZZI, Renata B.; FUJITA, André; TAKAHASHI, Daniel Y. Predicting soft robot's locomotion fitness. In: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION, 23., 2021, Lille, França. Proceedings […]. Nova Iorque: Association for Computing Machinery, 2021. p. 81-82. Disponível em: https://dl.acm.org/doi/10.1145/3449726.3459417. Acesso em: 6 ago. 21.https://repositorio.ufrn.br/handle/123456789/3304910.1145/3449726.3459417Evolutionary roboticsFitness evaluationHeuristicsComplex systemsTheoryPredicting soft robot’s locomotion fitnessinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjectOrganisms with different body morphology and movement dynamics have distinct abilities to move through the environment. Despite such truism, there is a lack of general principles that predict which shapes and dynamics make the organisms more fit to move. Studying a minimal yet embodied soft robot model under the influence of gravity, we find three features that predict robot locomotion fitness: (1) A larger body is better. (2) Two-point contact with the ground is better than one-point contact. (3) Out-of-phase oscillating body parts increase locomotion fitness. These design principles can guide the selection rules for evolutionary algorithms to obtain robots with higher locomotion fitnessengreponame:Repositório Institucional da UFRNinstname:Universidade Federal do Rio Grande do Norte (UFRN)instacron:UFRNinfo:eu-repo/semantics/openAccessORIGINALPoster_PredictingSof_Takahashi_2021.pdfPoster_PredictingSof_Takahashi_2021.pdfPoster_PredictingSof_Takahashi_2021application/pdf2694200https://repositorio.ufrn.br/bitstream/123456789/33049/1/Poster_PredictingSof_Takahashi_2021.pdf31d66e27a95bb0d1cc7842d6566ed354MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81569https://repositorio.ufrn.br/bitstream/123456789/33049/2/license.txt6e6f57145bc87daf99079f06b081ff9fMD52123456789/330492021-08-09 09:01:02.168oai:https://repositorio.ufrn.br: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ório de PublicaçõesPUBhttp://repositorio.ufrn.br/oai/opendoar:2021-08-09T12:01:02Repositório Institucional da UFRN - Universidade Federal do Rio Grande do Norte (UFRN)false
dc.title.pt_BR.fl_str_mv Predicting soft robot’s locomotion fitness
title Predicting soft robot’s locomotion fitness
spellingShingle Predicting soft robot’s locomotion fitness
Biazzi, Renata Biaggi
Evolutionary robotics
Fitness evaluation
Heuristics
Complex systems
Theory
title_short Predicting soft robot’s locomotion fitness
title_full Predicting soft robot’s locomotion fitness
title_fullStr Predicting soft robot’s locomotion fitness
title_full_unstemmed Predicting soft robot’s locomotion fitness
title_sort Predicting soft robot’s locomotion fitness
author Biazzi, Renata Biaggi
author_facet Biazzi, Renata Biaggi
Fujita, André
Takahashi, Daniel Yasumasa
author_role author
author2 Fujita, André
Takahashi, Daniel Yasumasa
author2_role author
author
dc.contributor.author.fl_str_mv Biazzi, Renata Biaggi
Fujita, André
Takahashi, Daniel Yasumasa
dc.subject.por.fl_str_mv Evolutionary robotics
Fitness evaluation
Heuristics
Complex systems
Theory
topic Evolutionary robotics
Fitness evaluation
Heuristics
Complex systems
Theory
description Organisms with different body morphology and movement dynamics have distinct abilities to move through the environment. Despite such truism, there is a lack of general principles that predict which shapes and dynamics make the organisms more fit to move. Studying a minimal yet embodied soft robot model under the influence of gravity, we find three features that predict robot locomotion fitness: (1) A larger body is better. (2) Two-point contact with the ground is better than one-point contact. (3) Out-of-phase oscillating body parts increase locomotion fitness. These design principles can guide the selection rules for evolutionary algorithms to obtain robots with higher locomotion fitness
publishDate 2021
dc.date.accessioned.fl_str_mv 2021-08-09T12:01:01Z
dc.date.available.fl_str_mv 2021-08-09T12:01:01Z
dc.date.issued.fl_str_mv 2021-07-07
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.citation.fl_str_mv BIAZZI, Renata B.; FUJITA, André; TAKAHASHI, Daniel Y. Predicting soft robot's locomotion fitness. In: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION, 23., 2021, Lille, França. Proceedings […]. Nova Iorque: Association for Computing Machinery, 2021. p. 81-82. Disponível em: https://dl.acm.org/doi/10.1145/3449726.3459417. Acesso em: 6 ago. 21.
dc.identifier.uri.fl_str_mv https://repositorio.ufrn.br/handle/123456789/33049
dc.identifier.doi.none.fl_str_mv 10.1145/3449726.3459417
identifier_str_mv BIAZZI, Renata B.; FUJITA, André; TAKAHASHI, Daniel Y. Predicting soft robot's locomotion fitness. In: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION, 23., 2021, Lille, França. Proceedings […]. Nova Iorque: Association for Computing Machinery, 2021. p. 81-82. Disponível em: https://dl.acm.org/doi/10.1145/3449726.3459417. Acesso em: 6 ago. 21.
10.1145/3449726.3459417
url https://repositorio.ufrn.br/handle/123456789/33049
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:Repositório Institucional da UFRN
instname:Universidade Federal do Rio Grande do Norte (UFRN)
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instname_str Universidade Federal do Rio Grande do Norte (UFRN)
instacron_str UFRN
institution UFRN
reponame_str Repositório Institucional da UFRN
collection Repositório Institucional da UFRN
bitstream.url.fl_str_mv https://repositorio.ufrn.br/bitstream/123456789/33049/1/Poster_PredictingSof_Takahashi_2021.pdf
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