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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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). |
format | Online Article Text |
id | pubmed-9775644 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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