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Coastal Areas Division and Coverage with Multiple UAVs for Remote Sensing
This paper tackles the problems of exact cell decomposition and partitioning of a coastal region for a team of heterogeneous Unmanned Aerial Vehicles (UAVs) with an approach that takes into account the field of view or sensing radius of the sensors on-board. An initial sensor-based exact cell decomp...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5422169/ https://www.ncbi.nlm.nih.gov/pubmed/28397775 http://dx.doi.org/10.3390/s17040808 |
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author | Balampanis, Fotios Maza, Iván Ollero, Aníbal |
author_facet | Balampanis, Fotios Maza, Iván Ollero, Aníbal |
author_sort | Balampanis, Fotios |
collection | PubMed |
description | This paper tackles the problems of exact cell decomposition and partitioning of a coastal region for a team of heterogeneous Unmanned Aerial Vehicles (UAVs) with an approach that takes into account the field of view or sensing radius of the sensors on-board. An initial sensor-based exact cell decomposition of the area aids in the partitioning process, which is performed in two steps. In the first step, a growing regions algorithm performs an isotropic partitioning of the area based on the initial locations of the UAVs and their relative capabilities. Then, two novel algorithms are applied to compute an adjustment of this partitioning process, in order to solve deadlock situations that generate non-allocated regions and sub-areas above or below the relative capabilities of the UAVs. Finally, realistic simulations have been conducted for the evaluation of the proposed solution, and the obtained results show that these algorithms can compute valid and sound solutions in complex coastal region scenarios under different setups for the UAVs. |
format | Online Article Text |
id | pubmed-5422169 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-54221692017-05-12 Coastal Areas Division and Coverage with Multiple UAVs for Remote Sensing Balampanis, Fotios Maza, Iván Ollero, Aníbal Sensors (Basel) Article This paper tackles the problems of exact cell decomposition and partitioning of a coastal region for a team of heterogeneous Unmanned Aerial Vehicles (UAVs) with an approach that takes into account the field of view or sensing radius of the sensors on-board. An initial sensor-based exact cell decomposition of the area aids in the partitioning process, which is performed in two steps. In the first step, a growing regions algorithm performs an isotropic partitioning of the area based on the initial locations of the UAVs and their relative capabilities. Then, two novel algorithms are applied to compute an adjustment of this partitioning process, in order to solve deadlock situations that generate non-allocated regions and sub-areas above or below the relative capabilities of the UAVs. Finally, realistic simulations have been conducted for the evaluation of the proposed solution, and the obtained results show that these algorithms can compute valid and sound solutions in complex coastal region scenarios under different setups for the UAVs. MDPI 2017-04-09 /pmc/articles/PMC5422169/ /pubmed/28397775 http://dx.doi.org/10.3390/s17040808 Text en © 2017 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Balampanis, Fotios Maza, Iván Ollero, Aníbal Coastal Areas Division and Coverage with Multiple UAVs for Remote Sensing |
title | Coastal Areas Division and Coverage with Multiple UAVs for Remote Sensing |
title_full | Coastal Areas Division and Coverage with Multiple UAVs for Remote Sensing |
title_fullStr | Coastal Areas Division and Coverage with Multiple UAVs for Remote Sensing |
title_full_unstemmed | Coastal Areas Division and Coverage with Multiple UAVs for Remote Sensing |
title_short | Coastal Areas Division and Coverage with Multiple UAVs for Remote Sensing |
title_sort | coastal areas division and coverage with multiple uavs for remote sensing |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5422169/ https://www.ncbi.nlm.nih.gov/pubmed/28397775 http://dx.doi.org/10.3390/s17040808 |
work_keys_str_mv | AT balampanisfotios coastalareasdivisionandcoveragewithmultipleuavsforremotesensing AT mazaivan coastalareasdivisionandcoveragewithmultipleuavsforremotesensing AT olleroanibal coastalareasdivisionandcoveragewithmultipleuavsforremotesensing |