Utilize este identificador para referenciar este registo:
http://hdl.handle.net/10071/23296
Autoria: | Ramos, R. Duarte, M. Oliveira, S. M. Christensen, A. L. |
Editor: | Tuci, E., Giagkos, A., Wilson, M., and Hallam, J. |
Data: | 2016 |
Título próprio: | Evolving controllers for robots with multimodal locomotion |
Volume: | 9825 |
Paginação: | 340 - 351 |
Título do evento: | 14th International Conference on Simulation of Adaptive Behavior, SAB 2016 |
ISBN: | 978-3-319-43488-9 |
DOI (Digital Object Identifier): | 10.1007/978-3-319-43488-9_30 |
Palavras-chave: | Evolutionary robotics Multimodal locomotion Navigation task |
Resumo: | Animals have inspired numerous studies on robot locomotion, but the problem of how autonomous robots can learn to take advantage of multimodal locomotion remains largely unexplored. In this paper, we study how a robot with two different means of locomotion can effective learn when to use each one based only on the limited information it can obtain through its onboard sensors. We conduct a series of simulation-based experiments using a task where a wheeled robot capable of jumping has to navigate to a target destination as quickly as possible in environments containing obstacles. We apply evolutionary techniques to synthesize neural controllers for the robot, and we analyze the evolved behaviors. The results show that the robot succeeds in learning when to drive and when to jump. The results also show that, compared with unimodal locomotion, multimodal locomotion allows for simpler and higher performing behaviors to evolve. |
Arbitragem científica: | yes |
Acesso: | Acesso Aberto |
Aparece nas coleções: | IT-CRI - Comunicações a conferências internacionais |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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conferenceobject_31609.pdf | Versão Aceite | 2,16 MB | Adobe PDF | Ver/Abrir |
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