Generation of gait trajectories for a humanoid robot from motion capture
In this research paper, we propose a method to adapt the gaits trajectories of a human being to a humanoid robot. To achieve this goal, first we made an experiments with with two different motion capture systems to obtain the joint trajectories of human walking. The trajectories obtained by motion c...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2021
- Institución:
- Universidad Pedagógica y Tecnológica de Colombia
- Repositorio:
- RiUPTC: Repositorio Institucional UPTC
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.uptc.edu.co:001/15307
- Acceso en línea:
- https://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/view/12446
https://repositorio.uptc.edu.co/handle/001/15307
- Palabra clave:
- Captura de movimiento
Generación de Trayectorias
Robótica humanoide
Marcha humana
Optimización
Motion capture
Trajectory Generation
Humanoid robotics
Humain gait
Optimization
- Rights
- License
- http://purl.org/coar/access_right/c_abf2
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2021-09-072024-07-08T14:24:02Z2024-07-08T14:24:02Zhttps://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/view/1244610.19053/01217488.v12.n2.2021.12446https://repositorio.uptc.edu.co/handle/001/15307In this research paper, we propose a method to adapt the gaits trajectories of a human being to a humanoid robot. To achieve this goal, first we made an experiments with with two different motion capture systems to obtain the joint trajectories of human walking. The trajectories obtained by motion capture serve as input to a trajectory generator off-line, called dynamic filter that takes into account the kinematic and dynamic constraints necessary to prevent the robot fall while walking. To validate the trajectories, a simulator was used for the robot Bioloid Premium Kit based on the V-Rep environment. After, these were implemented on the real robot. Even though the motion generation is done offline, the dynamics filter is a good option for making in automated way the trajectories generation.En este trabajo se propone un método para adaptar las trayectorias de marcha de un ser humano a un robot humanoide. Las trayectorias obtenidas mediante captura de movimiento sirven como entrada a un generador de trayectorias off-line, llamado filtro dinámico en el que se tienen en cuenta las restricciones cinemáticas y dinámicas necesarias para impedir que el robot caiga mientras camina. Para validar las trayectorias se uso un simulador para el robot Bioloid Premium kit basado en el entorno V-Rep y después se implementaron sobre el robot real. A pesar de que la generación de trayectorias se lleva a cabo fuera de línea, el filtro dinámico es una buena opción para generar trayectorias que permitan mantener la estabilidad del robot.application/pdfspaspaUniversidad Pedagógica y Tecnológica de Colombiahttps://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/view/12446/11160Ciencia En Desarrollo; Vol. 12 No. 2 (2021): Vol 12, Núm.2 (2021): Julio-DiciembreCiencia en Desarrollo; Vol. 12 Núm. 2 (2021): Vol 12, Núm.2 (2021): Julio-Diciembre2462-76580121-7488Captura de movimientoGeneración de TrayectoriasRobótica humanoideMarcha humanaOptimizaciónMotion captureTrajectory GenerationHumanoid roboticsHumain gaitOptimizationGeneration of gait trajectories for a humanoid robot from motion captureGeneración de trayectorias de marcha para un robot humanoide a partir de captura de movimientoinfo:eu-repo/semantics/articlehttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/access_right/c_abf2Bravo Montenegro, Diego AlbertoRengifo F, Carlos F001/15307oai:repositorio.uptc.edu.co:001/153072025-07-18 10:56:46.168metadata.onlyhttps://repositorio.uptc.edu.coRepositorio Institucional UPTCrepositorio.uptc@uptc.edu.co |
dc.title.en-US.fl_str_mv |
Generation of gait trajectories for a humanoid robot from motion capture |
dc.title.es-ES.fl_str_mv |
Generación de trayectorias de marcha para un robot humanoide a partir de captura de movimiento |
title |
Generation of gait trajectories for a humanoid robot from motion capture |
spellingShingle |
Generation of gait trajectories for a humanoid robot from motion capture Captura de movimiento Generación de Trayectorias Robótica humanoide Marcha humana Optimización Motion capture Trajectory Generation Humanoid robotics Humain gait Optimization |
title_short |
Generation of gait trajectories for a humanoid robot from motion capture |
title_full |
Generation of gait trajectories for a humanoid robot from motion capture |
title_fullStr |
Generation of gait trajectories for a humanoid robot from motion capture |
title_full_unstemmed |
Generation of gait trajectories for a humanoid robot from motion capture |
title_sort |
Generation of gait trajectories for a humanoid robot from motion capture |
dc.subject.es-ES.fl_str_mv |
Captura de movimiento Generación de Trayectorias Robótica humanoide Marcha humana Optimización |
topic |
Captura de movimiento Generación de Trayectorias Robótica humanoide Marcha humana Optimización Motion capture Trajectory Generation Humanoid robotics Humain gait Optimization |
dc.subject.en-US.fl_str_mv |
Motion capture Trajectory Generation Humanoid robotics Humain gait Optimization |
description |
In this research paper, we propose a method to adapt the gaits trajectories of a human being to a humanoid robot. To achieve this goal, first we made an experiments with with two different motion capture systems to obtain the joint trajectories of human walking. The trajectories obtained by motion capture serve as input to a trajectory generator off-line, called dynamic filter that takes into account the kinematic and dynamic constraints necessary to prevent the robot fall while walking. To validate the trajectories, a simulator was used for the robot Bioloid Premium Kit based on the V-Rep environment. After, these were implemented on the real robot. Even though the motion generation is done offline, the dynamics filter is a good option for making in automated way the trajectories generation. |
publishDate |
2021 |
dc.date.accessioned.none.fl_str_mv |
2024-07-08T14:24:02Z |
dc.date.available.none.fl_str_mv |
2024-07-08T14:24:02Z |
dc.date.none.fl_str_mv |
2021-09-07 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
dc.identifier.none.fl_str_mv |
https://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/view/12446 10.19053/01217488.v12.n2.2021.12446 |
dc.identifier.uri.none.fl_str_mv |
https://repositorio.uptc.edu.co/handle/001/15307 |
url |
https://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/view/12446 https://repositorio.uptc.edu.co/handle/001/15307 |
identifier_str_mv |
10.19053/01217488.v12.n2.2021.12446 |
dc.language.none.fl_str_mv |
spa |
dc.language.iso.none.fl_str_mv |
spa |
language |
spa |
dc.relation.none.fl_str_mv |
https://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/view/12446/11160 |
dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
rights_invalid_str_mv |
http://purl.org/coar/access_right/c_abf2 |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.es-ES.fl_str_mv |
Universidad Pedagógica y Tecnológica de Colombia |
dc.source.en-US.fl_str_mv |
Ciencia En Desarrollo; Vol. 12 No. 2 (2021): Vol 12, Núm.2 (2021): Julio-Diciembre |
dc.source.es-ES.fl_str_mv |
Ciencia en Desarrollo; Vol. 12 Núm. 2 (2021): Vol 12, Núm.2 (2021): Julio-Diciembre |
dc.source.none.fl_str_mv |
2462-7658 0121-7488 |
institution |
Universidad Pedagógica y Tecnológica de Colombia |
repository.name.fl_str_mv |
Repositorio Institucional UPTC |
repository.mail.fl_str_mv |
repositorio.uptc@uptc.edu.co |
_version_ |
1839633880832802816 |