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Dataset for the cumulative unmanned aerial vehicle routing problem

The Cumulative Unmanned Aerial Vehicle Routing Problem applies to area covering operations using UAVs. It is defined on a graph, the nodes of which, ensure the complete coverage of the underlying area of interest. The data generation process takes into account the characteristics of such operations,...

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Detalles Bibliográficos
Autores principales: Kyriakakis, Nikolaos A., Stamadianos, Themistoklis, Marinaki, Magdalene, Marinakis, Yannis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10294095/
https://www.ncbi.nlm.nih.gov/pubmed/37383775
http://dx.doi.org/10.1016/j.dib.2023.109296
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author Kyriakakis, Nikolaos A.
Stamadianos, Themistoklis
Marinaki, Magdalene
Marinakis, Yannis
author_facet Kyriakakis, Nikolaos A.
Stamadianos, Themistoklis
Marinaki, Magdalene
Marinakis, Yannis
author_sort Kyriakakis, Nikolaos A.
collection PubMed
description The Cumulative Unmanned Aerial Vehicle Routing Problem applies to area covering operations using UAVs. It is defined on a graph, the nodes of which, ensure the complete coverage of the underlying area of interest. The data generation process takes into account the characteristics of such operations, in particular, the viewing window of the UAVs’ sensor, their maximum range, the size of the UAV fleet and the unknown locations of the targets within the area of interest. Instances are created simulating different scenarios, using different values for those UAV characteristics, as well as the different locations where the search targets might be positioned in the area of interest.
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spelling pubmed-102940952023-06-28 Dataset for the cumulative unmanned aerial vehicle routing problem Kyriakakis, Nikolaos A. Stamadianos, Themistoklis Marinaki, Magdalene Marinakis, Yannis Data Brief Data Article The Cumulative Unmanned Aerial Vehicle Routing Problem applies to area covering operations using UAVs. It is defined on a graph, the nodes of which, ensure the complete coverage of the underlying area of interest. The data generation process takes into account the characteristics of such operations, in particular, the viewing window of the UAVs’ sensor, their maximum range, the size of the UAV fleet and the unknown locations of the targets within the area of interest. Instances are created simulating different scenarios, using different values for those UAV characteristics, as well as the different locations where the search targets might be positioned in the area of interest. Elsevier 2023-06-05 /pmc/articles/PMC10294095/ /pubmed/37383775 http://dx.doi.org/10.1016/j.dib.2023.109296 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Data Article
Kyriakakis, Nikolaos A.
Stamadianos, Themistoklis
Marinaki, Magdalene
Marinakis, Yannis
Dataset for the cumulative unmanned aerial vehicle routing problem
title Dataset for the cumulative unmanned aerial vehicle routing problem
title_full Dataset for the cumulative unmanned aerial vehicle routing problem
title_fullStr Dataset for the cumulative unmanned aerial vehicle routing problem
title_full_unstemmed Dataset for the cumulative unmanned aerial vehicle routing problem
title_short Dataset for the cumulative unmanned aerial vehicle routing problem
title_sort dataset for the cumulative unmanned aerial vehicle routing problem
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10294095/
https://www.ncbi.nlm.nih.gov/pubmed/37383775
http://dx.doi.org/10.1016/j.dib.2023.109296
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