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Sensors 2016, 16(3), 335; doi:10.3390/s16030335

Robot Guidance Using Machine Vision Techniques in Industrial Environments: A Comparative Review

1
Fundación PRODINTEC, Avda. Jardín Botánico 1345, 33203 Gijón (Asturias), Spain
2
Department of Computer Science and Engineering, Universidad de Oviedo, Campus de Viesques, 33203 Gijón (Asturias), Spain
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Gonzalo Pajares Martinsanz
Received: 13 January 2016 / Revised: 18 February 2016 / Accepted: 26 February 2016 / Published: 5 March 2016
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Spain 2015)
View Full-Text   |   Download PDF [7045 KB, uploaded 5 March 2016]   |  

Abstract

In the factory of the future, most of the operations will be done by autonomous robots that need visual feedback to move around the working space avoiding obstacles, to work collaboratively with humans, to identify and locate the working parts, to complete the information provided by other sensors to improve their positioning accuracy, etc. Different vision techniques, such as photogrammetry, stereo vision, structured light, time of flight and laser triangulation, among others, are widely used for inspection and quality control processes in the industry and now for robot guidance. Choosing which type of vision system to use is highly dependent on the parts that need to be located or measured. Thus, in this paper a comparative review of different machine vision techniques for robot guidance is presented. This work analyzes accuracy, range and weight of the sensors, safety, processing time and environmental influences. Researchers and developers can take it as a background information for their future works. View Full-Text
Keywords: machine vision; 3D sensors; perception for manipulation; robot guidance; robot pose; part localization machine vision; 3D sensors; perception for manipulation; robot guidance; robot pose; part localization
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Pérez, L.; Rodríguez, Í.; Rodríguez, N.; Usamentiaga, R.; García, D.F. Robot Guidance Using Machine Vision Techniques in Industrial Environments: A Comparative Review. Sensors 2016, 16, 335.

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