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Autonomous Multi-Robot Search for a Hazardous Source in a Turbulent Environment

Finding the source of an accidental or deliberate release of a toxic substance into the atmosphere is of great importance for national security. The paper presents a search algorithm for turbulent environments which falls into the class of cognitive (infotaxi) algorithms. Bayesian estimation of the...

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Detalles Bibliográficos
Autores principales: Ristic, Branko, Angley, Daniel, Moran, Bill, Palmer, Jennifer L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5428082/
https://www.ncbi.nlm.nih.gov/pubmed/28430120
http://dx.doi.org/10.3390/s17040918
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author Ristic, Branko
Angley, Daniel
Moran, Bill
Palmer, Jennifer L.
author_facet Ristic, Branko
Angley, Daniel
Moran, Bill
Palmer, Jennifer L.
author_sort Ristic, Branko
collection PubMed
description Finding the source of an accidental or deliberate release of a toxic substance into the atmosphere is of great importance for national security. The paper presents a search algorithm for turbulent environments which falls into the class of cognitive (infotaxi) algorithms. Bayesian estimation of the source parameter vector is carried out using the Rao–Blackwell dimension-reduction method, while the robots are controlled autonomously to move in a scalable formation. Estimation and control are carried out in a centralised replicated fusion architecture assuming all-to-all communication. The paper presents a comprehensive numerical analysis of the proposed algorithm, including the search-time and displacement statistics.
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spelling pubmed-54280822017-05-12 Autonomous Multi-Robot Search for a Hazardous Source in a Turbulent Environment Ristic, Branko Angley, Daniel Moran, Bill Palmer, Jennifer L. Sensors (Basel) Article Finding the source of an accidental or deliberate release of a toxic substance into the atmosphere is of great importance for national security. The paper presents a search algorithm for turbulent environments which falls into the class of cognitive (infotaxi) algorithms. Bayesian estimation of the source parameter vector is carried out using the Rao–Blackwell dimension-reduction method, while the robots are controlled autonomously to move in a scalable formation. Estimation and control are carried out in a centralised replicated fusion architecture assuming all-to-all communication. The paper presents a comprehensive numerical analysis of the proposed algorithm, including the search-time and displacement statistics. MDPI 2017-04-21 /pmc/articles/PMC5428082/ /pubmed/28430120 http://dx.doi.org/10.3390/s17040918 Text en © 2017 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
Ristic, Branko
Angley, Daniel
Moran, Bill
Palmer, Jennifer L.
Autonomous Multi-Robot Search for a Hazardous Source in a Turbulent Environment
title Autonomous Multi-Robot Search for a Hazardous Source in a Turbulent Environment
title_full Autonomous Multi-Robot Search for a Hazardous Source in a Turbulent Environment
title_fullStr Autonomous Multi-Robot Search for a Hazardous Source in a Turbulent Environment
title_full_unstemmed Autonomous Multi-Robot Search for a Hazardous Source in a Turbulent Environment
title_short Autonomous Multi-Robot Search for a Hazardous Source in a Turbulent Environment
title_sort autonomous multi-robot search for a hazardous source in a turbulent environment
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5428082/
https://www.ncbi.nlm.nih.gov/pubmed/28430120
http://dx.doi.org/10.3390/s17040918
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