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Line and V-Shape Formation Based Distributed Processing for Robotic Swarms

Efficient distributed processing is vital for collaborative searching tasks of robotic swarm systems. Typically, those systems are decentralized, and the members have only limited communication and processing capacities. What is illustrated in this paper is a distributed processing paradigm for robo...

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Detalles Bibliográficos
Autores principales: Yang, Jian, Wang, Xin, Bauer, Peter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6111279/
https://www.ncbi.nlm.nih.gov/pubmed/30081491
http://dx.doi.org/10.3390/s18082543
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author Yang, Jian
Wang, Xin
Bauer, Peter
author_facet Yang, Jian
Wang, Xin
Bauer, Peter
author_sort Yang, Jian
collection PubMed
description Efficient distributed processing is vital for collaborative searching tasks of robotic swarm systems. Typically, those systems are decentralized, and the members have only limited communication and processing capacities. What is illustrated in this paper is a distributed processing paradigm for robotic swarms moving in a line or v-shape formation. The introduced concept is capable of exploits the line and v-shape formations for 2-D filtering and processing algorithms based on a modified multi-dimensional Roesser model. The communication is only between nearest adjacent members with a simple state variable. As an example, we applied a salient region detection algorithm to the proposed framework. The simulation results indicate the designed paradigm can detect salient regions by using a moving line or v-shape formation in a scanning way. The requirement of communication and processing capability in this framework is minimal, making it a good candidate for collaborative exploration of formatted robotic swarms.
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spelling pubmed-61112792018-08-30 Line and V-Shape Formation Based Distributed Processing for Robotic Swarms Yang, Jian Wang, Xin Bauer, Peter Sensors (Basel) Article Efficient distributed processing is vital for collaborative searching tasks of robotic swarm systems. Typically, those systems are decentralized, and the members have only limited communication and processing capacities. What is illustrated in this paper is a distributed processing paradigm for robotic swarms moving in a line or v-shape formation. The introduced concept is capable of exploits the line and v-shape formations for 2-D filtering and processing algorithms based on a modified multi-dimensional Roesser model. The communication is only between nearest adjacent members with a simple state variable. As an example, we applied a salient region detection algorithm to the proposed framework. The simulation results indicate the designed paradigm can detect salient regions by using a moving line or v-shape formation in a scanning way. The requirement of communication and processing capability in this framework is minimal, making it a good candidate for collaborative exploration of formatted robotic swarms. MDPI 2018-08-03 /pmc/articles/PMC6111279/ /pubmed/30081491 http://dx.doi.org/10.3390/s18082543 Text en © 2018 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 (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yang, Jian
Wang, Xin
Bauer, Peter
Line and V-Shape Formation Based Distributed Processing for Robotic Swarms
title Line and V-Shape Formation Based Distributed Processing for Robotic Swarms
title_full Line and V-Shape Formation Based Distributed Processing for Robotic Swarms
title_fullStr Line and V-Shape Formation Based Distributed Processing for Robotic Swarms
title_full_unstemmed Line and V-Shape Formation Based Distributed Processing for Robotic Swarms
title_short Line and V-Shape Formation Based Distributed Processing for Robotic Swarms
title_sort line and v-shape formation based distributed processing for robotic swarms
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6111279/
https://www.ncbi.nlm.nih.gov/pubmed/30081491
http://dx.doi.org/10.3390/s18082543
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