Predicting soft robot’s locomotion fitness
| dc.contributor.author | Biazzi, Renata Biaggi | |
| dc.contributor.author | Fujita, André | |
| dc.contributor.author | Takahashi, Daniel Yasumasa | |
| dc.date.accessioned | 2021-08-09T12:01:01Z | |
| dc.date.available | 2021-08-09T12:01:01Z | |
| dc.date.issued | 2021-07-07 | |
| dc.description.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 | pt_BR |
| dc.identifier.citation | 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. | pt_BR |
| dc.identifier.doi | 10.1145/3449726.3459417 | |
| dc.identifier.uri | https://repositorio.ufrn.br/handle/123456789/33049 | |
| dc.language | en | pt_BR |
| dc.subject | Evolutionary robotics | pt_BR |
| dc.subject | Fitness evaluation | pt_BR |
| dc.subject | Heuristics | pt_BR |
| dc.subject | Complex systems | pt_BR |
| dc.subject | Theory | pt_BR |
| dc.title | Predicting soft robot’s locomotion fitness | pt_BR |
| dc.type | conferenceObject | pt_BR |
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