Red neuronal convolucional para la percepción espacial del robot InMoov a través de visión estereoscópica como tecnología de asistencia

dc.creatorCortes Zarta, Juan F.
dc.creatorGiraldo Tique, Yesica A.
dc.creatorVergara Ramírez, Carlos F.
dc.date.accessioned2023-08-17T16:28:28Z
dc.date.accessioned2023-09-06T19:31:57Z
dc.date.available2023-08-17T16:28:28Z
dc.date.available2023-09-06T19:31:57Z
dc.date.created2023-08-17T16:28:28Z
dc.date.issued2021-10-01
dc.identifierhttps://hdl.handle.net/20.500.14329/586
dc.identifier1390-6542
dc.identifierEscuela Tecnológica Instituto Técnico Central
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8702347
dc.description.abstractEn el desarrollo de los robots de asistencia un reto importante consiste en mejorar la percepción espacial de los robots para la identificación de objetos en diversos escenarios. Para ello, es preciso desarrollar herramientas de análisis y procesamiento de datos de visión estereoscópica artificial. Por esta razón, el presente artículo describe un algoritmo de redes neuronales convolucionales (CNN) implementado en una Raspberry Pi 3 ubicada en la cabeza de una réplica del robot humanoide de código abierto InMoov para estimar la posición en X, Y, Z de un objeto dentro de un entorno controlado. Este artículo explica la construcción de la parte superior del robot InMoov, la aplicación de Trans fer Learning para detectar y segmentar un objeto dentro de un entorno controlado, el desarrollo de la arquitectura CNN y, por último, la asignación y evaluación de parámetros de entrenamiento. Como resultado, se obtuvo un error promedio estimado de 27 mm en la coordenada X, 21 mm en la coordenada Y y 4 mm en la coordenada Z. Estos datos son de gran impacto y necesarios al momento de usar esas coordenadas en un brazo robótico para que alcance el objeto y lo agarre, tema que queda pendiente para un futuro trabajo.
dc.description.abstractIn the development of assistive robots, a major challenge is to improve the spatial perception of robots for object identification in various scenarios. For this purpose, it is necessary to develop tools for analysis and processing of artificial stereo vision data. For this reason, this paper describes a convolutional neural network (CNN) algorithm implemented on a Raspberry Pi 3, placed on the head of a replica of the open-source humanoid robot InMoov, to estimate the X, Y, Z position of an object within a controlled environment. This paper explains the construction of the InMoov robot head, the application of Transfer Learning to detect and segment an object within a controlled environ ment, the development of the CNN architecture, and, finally, the assignment and evaluation of training parameters. As a result, an estimated average error of 27 mm in the X coordinate, 21 mm in the Y coordinate, and 4 mm in the Z coordinate was obtained; data of great impact and necessary when using these coordinates in a robotic arm to reach and grab the object, a topic that remains pending for future work
dc.languagespa
dc.publisherEscuela Tecnológica Instituto Técnico Central
dc.publisherBogotá
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dc.rightshttps://creativecommons.org/licenses/by-nc/4.0/
dc.rightsAtribución-NoComercial 4.0 Internacional (CC BY-NC 4.0)
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.titleRed neuronal convolucional para la percepción espacial del robot InMoov a través de visión estereoscópica como tecnología de asistencia
dc.typeArtículo de revista


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