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https://repositorio.ufrn.br/handle/123456789/33049| Título: | Predicting soft robot’s locomotion fitness |
| Autor(es): | Biazzi, Renata Biaggi Fujita, André Takahashi, Daniel Yasumasa |
| Palavras-chave: | Evolutionary robotics;Fitness evaluation;Heuristics;Complex systems;Theory |
| Data do documento: | 7-Jul-2021 |
| Referência: | 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. |
| 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 |
| URI: | https://repositorio.ufrn.br/handle/123456789/33049 |
| Aparece nas coleções: | ICe - Trabalhos apresentados em eventos |
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| Arquivo | Descrição | Tamanho | Formato | |
|---|---|---|---|---|
| Poster_PredictingSof_Takahashi_2021.pdf | Poster_PredictingSof_Takahashi_2021 | 2,63 MB | Adobe PDF | Visualizar/Abrir |
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