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Multi-Robot Task Scheduling with Ant Colony Optimization in Antarctic Environments

This paper addresses the problem of multi-robot task scheduling in Antarctic environments. There are various algorithms for multi-robot task scheduling, but there is a risk in robot operation when applied in Antarctic environments. This paper proposes a practical multi-robot scheduling method using...

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Detalles Bibliográficos
Autores principales: Kim, Seokyoung, Lee, Heoncheol
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9866026/
https://www.ncbi.nlm.nih.gov/pubmed/36679544
http://dx.doi.org/10.3390/s23020751
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author Kim, Seokyoung
Lee, Heoncheol
author_facet Kim, Seokyoung
Lee, Heoncheol
author_sort Kim, Seokyoung
collection PubMed
description This paper addresses the problem of multi-robot task scheduling in Antarctic environments. There are various algorithms for multi-robot task scheduling, but there is a risk in robot operation when applied in Antarctic environments. This paper proposes a practical multi-robot scheduling method using ant colony optimization in Antarctic environments. The proposed method was tested in both simulated and real Antarctic environments, and it was analyzed and compared with other existing algorithms. The improved performance of the proposed method was verified by finding more efficiently scheduled multiple paths with lower costs than the other algorithms.
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spelling pubmed-98660262023-01-22 Multi-Robot Task Scheduling with Ant Colony Optimization in Antarctic Environments Kim, Seokyoung Lee, Heoncheol Sensors (Basel) Article This paper addresses the problem of multi-robot task scheduling in Antarctic environments. There are various algorithms for multi-robot task scheduling, but there is a risk in robot operation when applied in Antarctic environments. This paper proposes a practical multi-robot scheduling method using ant colony optimization in Antarctic environments. The proposed method was tested in both simulated and real Antarctic environments, and it was analyzed and compared with other existing algorithms. The improved performance of the proposed method was verified by finding more efficiently scheduled multiple paths with lower costs than the other algorithms. MDPI 2023-01-09 /pmc/articles/PMC9866026/ /pubmed/36679544 http://dx.doi.org/10.3390/s23020751 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kim, Seokyoung
Lee, Heoncheol
Multi-Robot Task Scheduling with Ant Colony Optimization in Antarctic Environments
title Multi-Robot Task Scheduling with Ant Colony Optimization in Antarctic Environments
title_full Multi-Robot Task Scheduling with Ant Colony Optimization in Antarctic Environments
title_fullStr Multi-Robot Task Scheduling with Ant Colony Optimization in Antarctic Environments
title_full_unstemmed Multi-Robot Task Scheduling with Ant Colony Optimization in Antarctic Environments
title_short Multi-Robot Task Scheduling with Ant Colony Optimization in Antarctic Environments
title_sort multi-robot task scheduling with ant colony optimization in antarctic environments
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9866026/
https://www.ncbi.nlm.nih.gov/pubmed/36679544
http://dx.doi.org/10.3390/s23020751
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