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Odor source localization of multi-robots with swarm intelligence algorithms: A review
The use of robot swarms for odor source localization (OSL) can better adapt to the reality of unstable turbulence and find chemical contamination or hazard sources faster. Inspired by the collective behavior in nature, swarm intelligence (SI) is recognized as an appropriate algorithm framework for m...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9748100/ https://www.ncbi.nlm.nih.gov/pubmed/36531914 http://dx.doi.org/10.3389/fnbot.2022.949888 |
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author | Wang, Junhan Lin, Yuezhang Liu, Ruirui Fu, Jun |
author_facet | Wang, Junhan Lin, Yuezhang Liu, Ruirui Fu, Jun |
author_sort | Wang, Junhan |
collection | PubMed |
description | The use of robot swarms for odor source localization (OSL) can better adapt to the reality of unstable turbulence and find chemical contamination or hazard sources faster. Inspired by the collective behavior in nature, swarm intelligence (SI) is recognized as an appropriate algorithm framework for multi-robot system due to its parallelism, scalability and robustness. Applications of SI-based multi-robots for OSL problems have attracted great interest over the last two decades. In this review, we firstly summarize the trending issues in general robot OSL field through comparing some basic counterpart concepts, and then provide a detailed survey of various representative SI algorithms in multi-robot system for odor source localization. The research field originates from the first introduction of the standard particle swarm optimization (PSO) and flourishes in applying ever-increasing quantity of its variants as modified PSOs and hybrid PSOs. Moreover, other nature-inspired SI algorithms have also demonstrated the diversity and exploration of this field. The computer simulations and real-world applications reported in the literatures show that those algorithms could well solve the main problems of odor source localization but still retain the potential for further development. Lastly, we provide an outlook on possible future research directions. |
format | Online Article Text |
id | pubmed-9748100 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97481002022-12-15 Odor source localization of multi-robots with swarm intelligence algorithms: A review Wang, Junhan Lin, Yuezhang Liu, Ruirui Fu, Jun Front Neurorobot Neuroscience The use of robot swarms for odor source localization (OSL) can better adapt to the reality of unstable turbulence and find chemical contamination or hazard sources faster. Inspired by the collective behavior in nature, swarm intelligence (SI) is recognized as an appropriate algorithm framework for multi-robot system due to its parallelism, scalability and robustness. Applications of SI-based multi-robots for OSL problems have attracted great interest over the last two decades. In this review, we firstly summarize the trending issues in general robot OSL field through comparing some basic counterpart concepts, and then provide a detailed survey of various representative SI algorithms in multi-robot system for odor source localization. The research field originates from the first introduction of the standard particle swarm optimization (PSO) and flourishes in applying ever-increasing quantity of its variants as modified PSOs and hybrid PSOs. Moreover, other nature-inspired SI algorithms have also demonstrated the diversity and exploration of this field. The computer simulations and real-world applications reported in the literatures show that those algorithms could well solve the main problems of odor source localization but still retain the potential for further development. Lastly, we provide an outlook on possible future research directions. Frontiers Media S.A. 2022-11-30 /pmc/articles/PMC9748100/ /pubmed/36531914 http://dx.doi.org/10.3389/fnbot.2022.949888 Text en Copyright © 2022 Wang, Lin, Liu and Fu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Wang, Junhan Lin, Yuezhang Liu, Ruirui Fu, Jun Odor source localization of multi-robots with swarm intelligence algorithms: A review |
title | Odor source localization of multi-robots with swarm intelligence algorithms: A review |
title_full | Odor source localization of multi-robots with swarm intelligence algorithms: A review |
title_fullStr | Odor source localization of multi-robots with swarm intelligence algorithms: A review |
title_full_unstemmed | Odor source localization of multi-robots with swarm intelligence algorithms: A review |
title_short | Odor source localization of multi-robots with swarm intelligence algorithms: A review |
title_sort | odor source localization of multi-robots with swarm intelligence algorithms: a review |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9748100/ https://www.ncbi.nlm.nih.gov/pubmed/36531914 http://dx.doi.org/10.3389/fnbot.2022.949888 |
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