Navegación autónoma del Robot KUKA Youbot con señalización basada en técnicas de detección de objetos y redes neuronales profundas

The processes of exploration and autonomous navigation implemented in ROS allow to generate routes depending on the space that the environment presents, however, it does not take decisions based on specific objects that are in the space since it does not incorporate by default a system of vision by...

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Bibliographic Details
Published in:RISTI : Revista Ibérica de Sistemas e Tecnologias de Informação no. E19; pp. 305 - 316
Main Authors: Encalada, Patricio, Gordón, Carlos, Lema, Henry, León, Diego, Chicaiza, Dennis
Format: Journal Article
Language:Spanish
Published: Lousada Associação Ibérica de Sistemas e Tecnologias de Informacao 01.04.2019
Subjects:
Algorithms
Artificial intelligence
Artificial neural networks
Automation
Autonomous navigation
Deep learning
International conferences
Localization
Machine learning
Neural networks
Object recognition
Operating systems
Pattern recognition
Robotics
Robots
Software
ISSN:1646-9895
Online Access:Get full text
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Summary:The processes of exploration and autonomous navigation implemented in ROS allow to generate routes depending on the space that the environment presents, however, it does not take decisions based on specific objects that are in the space since it does not incorporate by default a system of vision by computer that allows it to obtain data. Keywords: Deep Learning; autonomous navigation; detection objects; KUKA YouBot; Robot Operating System; Python. 1.Introducción Los robots móviles autónomos están causando un cambio de paradigma en la forma en que concebimos los vehículos comerciales en industriales. En el texto Autonomous robot navigation based on pattern recognition techniques and artificial neural networks, de Quiñonez et al. Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (1 edition). Autonomous Robot Navigation Based on Pattern Recognition Techniques and Artificial Neural Networks.
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ISSN:1646-9895