Automatic Learning Improves Human-Robot Interaction in Productive Environments: A Review

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Title: Automatic Learning Improves Human-Robot Interaction in Productive Environments: A Review
Authors: Zamora Hernández, Mauricio Andrés | Caldwell Marín, Eldon Glen | Garcia-Rodriguez, Jose | Azorin-Lopez, Jorge | Cazorla, Miguel
Research Group/s: Informática Industrial y Redes de Computadores | Robótica y Visión Tridimensional (RoViT)
Center, Department or Service: Universidad de Alicante. Departamento de Tecnología Informática y Computación | Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial
Keywords: Augmented Reality | Computer Vision | Machine Learning | Manufacturing | Robotics
Knowledge Area: Arquitectura y Tecnología de Computadores | Ciencia de la Computación e Inteligencia Artificial
Issue Date: 2017
Publisher: IGI Global
Citation: International Journal of Computer Vision and Image Processing. 2017, 7(3): 65-75. doi:10.4018/IJCVIP.2017070106
Abstract: In the creation of new industries, products and services -- all of which are advances of the Fourth Industrial Revolution -- the human-robot interaction that includes automatic learning and computer vision are elements to consider since they promote collaborative environments between people and robots. The use of machine learning and computer vision provides the tools needed to increase productivity and minimizes delivery reaction times by assisting in the optimization of complex production planning processes. This review of the state of the art presents the main trends that seek to improve human-robot interaction in productive environments, and identifies challenges in research as well as in industrial - technological development in this topic. In addition, this review offers a proposal on the needs of use of artificial intelligence in all processes of industry 4.0 as a crucial linking element among humans, robots, intelligent and traditional machines; as well as a mechanism for quality control and occupational safety.
Sponsor: This work has been funded by the Spanish Government [TIN2016-76515-R] grant for the COMBAHO project, supported with Feder funds.
ISSN: 2155-6997 (Print) | 2155-6989 (Online)
DOI: 10.4018/IJCVIP.2017070106
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2017, IGI Global
Peer Review: si
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INV - RoViT - Artículos de Revistas
INV - AIA - Artículos de Revistas

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