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Improving Social Odometry Robot Networks with Distributed Reputation Systems for Collaborative Purposes

The improvement of odometry systems in collaborative robotics remains an important challenge for several applications. Social odometry is a social technique which confers the robots the possibility to learn from the others. This paper analyzes social odometry and proposes and follows a methodology t...

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Detalles Bibliográficos
Autores principales: Fraga, David, Gutiérrez, Álvaro, Vallejo, Juan Carlos, Campo, Alexandre, Bankovic, Zorana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Molecular Diversity Preservation International (MDPI) 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3251988/
https://www.ncbi.nlm.nih.gov/pubmed/22247671
http://dx.doi.org/10.3390/s111211372
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author Fraga, David
Gutiérrez, Álvaro
Vallejo, Juan Carlos
Campo, Alexandre
Bankovic, Zorana
author_facet Fraga, David
Gutiérrez, Álvaro
Vallejo, Juan Carlos
Campo, Alexandre
Bankovic, Zorana
author_sort Fraga, David
collection PubMed
description The improvement of odometry systems in collaborative robotics remains an important challenge for several applications. Social odometry is a social technique which confers the robots the possibility to learn from the others. This paper analyzes social odometry and proposes and follows a methodology to improve its behavior based on cooperative reputation systems. We also provide a reference implementation that allows us to compare the performance of the proposed solution in highly dynamic environments with the performance of standard social odometry techniques. Simulation results quantitatively show the benefits of this collaborative approach that allows us to achieve better performances than social odometry.
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spelling pubmed-32519882012-01-13 Improving Social Odometry Robot Networks with Distributed Reputation Systems for Collaborative Purposes Fraga, David Gutiérrez, Álvaro Vallejo, Juan Carlos Campo, Alexandre Bankovic, Zorana Sensors (Basel) Article The improvement of odometry systems in collaborative robotics remains an important challenge for several applications. Social odometry is a social technique which confers the robots the possibility to learn from the others. This paper analyzes social odometry and proposes and follows a methodology to improve its behavior based on cooperative reputation systems. We also provide a reference implementation that allows us to compare the performance of the proposed solution in highly dynamic environments with the performance of standard social odometry techniques. Simulation results quantitatively show the benefits of this collaborative approach that allows us to achieve better performances than social odometry. Molecular Diversity Preservation International (MDPI) 2011-11-30 /pmc/articles/PMC3251988/ /pubmed/22247671 http://dx.doi.org/10.3390/s111211372 Text en © 2011 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Article
Fraga, David
Gutiérrez, Álvaro
Vallejo, Juan Carlos
Campo, Alexandre
Bankovic, Zorana
Improving Social Odometry Robot Networks with Distributed Reputation Systems for Collaborative Purposes
title Improving Social Odometry Robot Networks with Distributed Reputation Systems for Collaborative Purposes
title_full Improving Social Odometry Robot Networks with Distributed Reputation Systems for Collaborative Purposes
title_fullStr Improving Social Odometry Robot Networks with Distributed Reputation Systems for Collaborative Purposes
title_full_unstemmed Improving Social Odometry Robot Networks with Distributed Reputation Systems for Collaborative Purposes
title_short Improving Social Odometry Robot Networks with Distributed Reputation Systems for Collaborative Purposes
title_sort improving social odometry robot networks with distributed reputation systems for collaborative purposes
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3251988/
https://www.ncbi.nlm.nih.gov/pubmed/22247671
http://dx.doi.org/10.3390/s111211372
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