Skip navigation
Use este identificador para citar ou linkar para este item: http://repositorio.unb.br/handle/10482/54187
Arquivos associados a este item:
Arquivo Descrição TamanhoFormato 
ARTIGO_VisualAidedObstacle.pdf31,67 MBAdobe PDFVisualizar/Abrir
Título: Visual-aided obstacle climbing by modular snake robot
Autor(es): Koike, Carla Maria Chagas e Cavalcante
Viana, Dianne Magalhães
Yudi, Jones
Batista, Filipe Aziz
Costa, Arthur Reichert
Carvalho, Vinícius
Rocha, Thiago de Deus Lima
ORCID: https://orcid.org/0000-0002-3641-1819
https://orcid.org/0000-0001-8396-8022
https://orcid.org/0000-0001-6707-853X
https://orcid.org/0000-0001-9553-3114
https://orcid.org/0009-0009-3919-693X
https://orcid.org/0009-0008-6533-5975
Afiliação do autor: University of Brasília, Department of Computer Science
University of Brasília, Department of Mechanical Engineering
University of Brasília, Department of Mechanical Engineering
University of Brasília, Mechatronics Graduate Programme
University of Brasília, Mechatronics Graduate Programme
University of Brasília, Department of Mechanical Engineering
University of Brasília, Department of Mechanical Engineering
Assunto: Robôs-serpente
Robôs escaladores
Robôs móveis
Locomoção guiada por imagem
Data de publicação: jul-2024
Editora: MDPI
Referência: KOIKE, Carla Cavalcante et. al. Visual-aided obstacle climbing by modular snake robot. Sensors, [S. l.], v. 24 , 2024. DOI: https://doi.org/10.3390/s24134424. Disponível em: https://www.mdpi.com/1424-8220/24/13/4424. Acesso em: 04 mar. 2026.
Abstract: Snake robots, also known as apodal robots, are among the most common and versatile modular robots. Primarily due to their ability to move in different patterns, they can evolve in scenarios with several constraints, some of them hardly accessible to other robot configurations. This paper deals with a specific environment constraint where the robot needs to climb a prismatic obstacle, similar to a step. The objective is to carry out simulations of this function, before implementing it in the physical model. To this end, we propose two different algorithms, parameterized by the obstacle dimensions determined by image processing, and both are evaluated in simulated experiments. The results show that both algorithms are viable for testing in real robots, although more complex scenarios still need to be further studied.
Unidade Acadêmica: Instituto de Ciências Exatas (IE)
Departamento de Ciência da Computação (IE CIC)
Faculdade de Tecnologia (FT)
Departamento de Engenharia Mecânica (FT ENM)
Licença: (CC-BY) Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
DOI: https://doi.org/10.3390/s24134424
Versão da editora: https://www.mdpi.com/1424-8220/24/13/4424
Aparece nas coleções:Artigos publicados em periódicos e afins

Mostrar registro completo do item Visualizar estatísticas



Os itens no repositório estão protegidos por copyright, com todos os direitos reservados, salvo quando é indicado o contrário.