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Path Planning with Time Windows for Multiple UAVs Based on Gray Wolf Algorithm

The Gray Wolf (GWO) algorithm aims to address the path planning problem of multiple UAVs, and the scene setting is mainly to avoid threats, meet the constraints of UAVs themselves and avoid obstacles between UAVs. The scene setting is relatively simple. To address such problems, the problem of time...

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Detalles Bibliográficos
Autores principales: Zhang, Changchun, Liu, Yifan, Hu, Chunhe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9775644/
https://www.ncbi.nlm.nih.gov/pubmed/36546924
http://dx.doi.org/10.3390/biomimetics7040225
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author Zhang, Changchun
Liu, Yifan
Hu, Chunhe
author_facet Zhang, Changchun
Liu, Yifan
Hu, Chunhe
author_sort Zhang, Changchun
collection PubMed
description The Gray Wolf (GWO) algorithm aims to address the path planning problem of multiple UAVs, and the scene setting is mainly to avoid threats, meet the constraints of UAVs themselves and avoid obstacles between UAVs. The scene setting is relatively simple. To address such problems, the problem of time windows is considered in this paper, so that the UAV can arrive at the same time, and the Gray Wolf algorithm is used to optimize the problem. Finally, the experimental results verify that the proposed method can plan a safe flight path in the process of multi-UAV flight and reach the goal point at the same time. The mean error of flight time between UAVs of the GWO is 0.213, which is superior to PSO (0.382), AFO (0.315) and GA (0.825).
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spelling pubmed-97756442022-12-23 Path Planning with Time Windows for Multiple UAVs Based on Gray Wolf Algorithm Zhang, Changchun Liu, Yifan Hu, Chunhe Biomimetics (Basel) Article The Gray Wolf (GWO) algorithm aims to address the path planning problem of multiple UAVs, and the scene setting is mainly to avoid threats, meet the constraints of UAVs themselves and avoid obstacles between UAVs. The scene setting is relatively simple. To address such problems, the problem of time windows is considered in this paper, so that the UAV can arrive at the same time, and the Gray Wolf algorithm is used to optimize the problem. Finally, the experimental results verify that the proposed method can plan a safe flight path in the process of multi-UAV flight and reach the goal point at the same time. The mean error of flight time between UAVs of the GWO is 0.213, which is superior to PSO (0.382), AFO (0.315) and GA (0.825). MDPI 2022-12-03 /pmc/articles/PMC9775644/ /pubmed/36546924 http://dx.doi.org/10.3390/biomimetics7040225 Text en © 2022 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
Zhang, Changchun
Liu, Yifan
Hu, Chunhe
Path Planning with Time Windows for Multiple UAVs Based on Gray Wolf Algorithm
title Path Planning with Time Windows for Multiple UAVs Based on Gray Wolf Algorithm
title_full Path Planning with Time Windows for Multiple UAVs Based on Gray Wolf Algorithm
title_fullStr Path Planning with Time Windows for Multiple UAVs Based on Gray Wolf Algorithm
title_full_unstemmed Path Planning with Time Windows for Multiple UAVs Based on Gray Wolf Algorithm
title_short Path Planning with Time Windows for Multiple UAVs Based on Gray Wolf Algorithm
title_sort path planning with time windows for multiple uavs based on gray wolf algorithm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9775644/
https://www.ncbi.nlm.nih.gov/pubmed/36546924
http://dx.doi.org/10.3390/biomimetics7040225
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