Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Guelis Montenegro"'
Autor:
Karla Schröder, Gonzalo Garcia, Roberto Chacón, Guelis Montenegro, Alberto Marroquín, Gonzalo Farias, Sebastián Dormido-Canto, Ernesto Fabregas
Publikováno v:
Sensors, Vol 23, Iss 8, p 3895 (2023)
This paper presents the design and implementation of a spherical robot with an internal mechanism based on a pendulum. The design is based on significant improvements made, including an electronics upgrade, to a previous robot prototype developed in
Externí odkaz:
https://doaj.org/article/4ab74ba1fb664d5998173a8d0d622053
Autor:
Gonzalo Farias, Gonzalo Garcia, Guelis Montenegro, Ernesto Fabregas, Sebastian Dormido-Canto, Sebastian Dormido
Publikováno v:
IEEE Access, Vol 8, Pp 152941-152951 (2020)
Due to the increase in complexity in autonomous vehicles, most of the existing control systems are proving to be inadequate. Reinforcement Learning is gaining traction as it is posed to overcome these difficulties in a natural way. This approach allo
Externí odkaz:
https://doaj.org/article/059b1c563cff4628b5d03d9966864732
Autor:
Guelis Montenegro, Roberto Chacón, Ernesto Fabregas, Gonzalo Garcia, Karla Schröder, Alberto Marroquín, Sebastián Dormido-Canto, Gonzalo Farias
Publikováno v:
Sensors, Vol 22, Iss 16, p 6020 (2022)
This article presents the development of a model of a spherical robot that rolls to move and has a single point of support with the surface. The model was developed in the CoppeliaSim simulator, which is a versatile tool for implementing this kind of
Externí odkaz:
https://doaj.org/article/0763fb93136d4ed99b593819c588d10d
Publikováno v:
Applied Sciences, Vol 12, Iss 14, p 7194 (2022)
This article proposes the use of reinforcement learning (RL) algorithms to control the position of a simulated Kephera IV mobile robot in a virtual environment. The simulated environment uses the OpenAI Gym library in conjunction with CoppeliaSim, a
Externí odkaz:
https://doaj.org/article/5c3645bd755e44e896dca29532055c26
Autor:
Guelis Montenegro, Sebastián Dormido-Canto, Ernesto Fabregas, Gonzalo Garcia, Gonzalo Farias, Sebastián Dormido
Publikováno v:
IEEE Access, Vol 8, Pp 152941-152951 (2020)
Due to the increase in complexity in autonomous vehicles, most of the existing control systems are proving to be inadequate. Reinforcement Learning is gaining traction as it is posed to overcome these difficulties in a natural way. This approach allo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1126a8e542dcc49789e06fc048b82c09
https://ieeexplore.ieee.org/document/9171241/
https://ieeexplore.ieee.org/document/9171241/