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UAV-UGV-UMV Multi-Swarms for Cooperative Surveillance
In this paper we present a surveillance system for early detection of escapers from a restricted area based on a new swarming mobility model called CROMM-MS (Chaotic Rössler Mobility Model for Multi-Swarms). CROMM-MS is designed for controlling the trajectories of heterogeneous multi-swarms of aeria...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7933201/ https://www.ncbi.nlm.nih.gov/pubmed/33681299 http://dx.doi.org/10.3389/frobt.2021.616950 |
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author | Stolfi, Daniel H. Brust, Matthias R. Danoy, Grégoire Bouvry, Pascal |
author_facet | Stolfi, Daniel H. Brust, Matthias R. Danoy, Grégoire Bouvry, Pascal |
author_sort | Stolfi, Daniel H. |
collection | PubMed |
description | In this paper we present a surveillance system for early detection of escapers from a restricted area based on a new swarming mobility model called CROMM-MS (Chaotic Rössler Mobility Model for Multi-Swarms). CROMM-MS is designed for controlling the trajectories of heterogeneous multi-swarms of aerial, ground and marine unmanned vehicles with important features such as prioritising early detections and success rate. A new Competitive Coevolutionary Genetic Algorithm (CompCGA) is proposed to optimise the vehicles’ parameters and escapers’ evasion ability using a predator-prey approach. Our results show that CROMM-MS is not only viable for surveillance tasks but also that its results are competitive in regard to the state-of-the-art approaches. |
format | Online Article Text |
id | pubmed-7933201 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-79332012021-03-06 UAV-UGV-UMV Multi-Swarms for Cooperative Surveillance Stolfi, Daniel H. Brust, Matthias R. Danoy, Grégoire Bouvry, Pascal Front Robot AI Robotics and AI In this paper we present a surveillance system for early detection of escapers from a restricted area based on a new swarming mobility model called CROMM-MS (Chaotic Rössler Mobility Model for Multi-Swarms). CROMM-MS is designed for controlling the trajectories of heterogeneous multi-swarms of aerial, ground and marine unmanned vehicles with important features such as prioritising early detections and success rate. A new Competitive Coevolutionary Genetic Algorithm (CompCGA) is proposed to optimise the vehicles’ parameters and escapers’ evasion ability using a predator-prey approach. Our results show that CROMM-MS is not only viable for surveillance tasks but also that its results are competitive in regard to the state-of-the-art approaches. Frontiers Media S.A. 2021-02-19 /pmc/articles/PMC7933201/ /pubmed/33681299 http://dx.doi.org/10.3389/frobt.2021.616950 Text en Copyright © 2021 Stolfi, Brust, Danoy and Bouvry. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Robotics and AI Stolfi, Daniel H. Brust, Matthias R. Danoy, Grégoire Bouvry, Pascal UAV-UGV-UMV Multi-Swarms for Cooperative Surveillance |
title | UAV-UGV-UMV Multi-Swarms for Cooperative Surveillance |
title_full | UAV-UGV-UMV Multi-Swarms for Cooperative Surveillance |
title_fullStr | UAV-UGV-UMV Multi-Swarms for Cooperative Surveillance |
title_full_unstemmed | UAV-UGV-UMV Multi-Swarms for Cooperative Surveillance |
title_short | UAV-UGV-UMV Multi-Swarms for Cooperative Surveillance |
title_sort | uav-ugv-umv multi-swarms for cooperative surveillance |
topic | Robotics and AI |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7933201/ https://www.ncbi.nlm.nih.gov/pubmed/33681299 http://dx.doi.org/10.3389/frobt.2021.616950 |
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