LSAR: Multi-UAV Collaboration for Search and Rescue Missions

Detalhes bibliográficos
Autor(a) principal: Alotaibi, Ebtehal Turki
Data de Publicação: 2019
Outros Autores: Saleh Alqefari, Shahad, Koubaa, Anis
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.22/13852
Resumo: In this paper, we consider the use of a team of multiple unmanned aerial vehicles (UAVs) to accomplish a search and rescue (SAR) mission in the minimum time possible while saving the maximum number of people. A novel technique for the SAR problem is proposed and referred to as the layered search and rescue (LSAR) algorithm. The novelty of LSAR involves simulating real disasters to distribute SAR tasks among UAVs. The performance of LSAR is compared, in terms of percentage of rescued survivors and rescue and execution times, with the max-sum, auction-based, and locust-inspired approaches for multi UAV task allocation (LIAM) and opportunistic task allocation (OTA) schemes. The simulation results show that the UAVs running the LSAR algorithm on average rescue approximately 74% of the survivors, which is 8% higher than the next best algorithm (LIAM). Moreover, this percentage increases with the number of UAVs, almost linearly with the least slope, which means more scalability and coverage is obtained in comparison to other algorithms. In addition, the empirical cumulative distribution function of LSAR results shows that the percentages of rescued survivors clustered around the [78% 100%] range under an exponential curve, meaning most results are above 50%. In comparison, all the other algorithms have almost equal distributions of their percentage of rescued survivor results. Furthermore, because the LSAR algorithm focuses on the center of the disaster, it nds more survivors and rescues them faster than the other algorithms, with an average of 55% 77%. Moreover, most registered times to rescue survivors by LSAR are bounded by a time of 04:50:02 with 95% con dence for a one-month mission time.
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spelling LSAR: Multi-UAV Collaboration for Search and Rescue MissionsAutonomous agentsDronesSearch and rescueUnmanned aerial vehiclesIn this paper, we consider the use of a team of multiple unmanned aerial vehicles (UAVs) to accomplish a search and rescue (SAR) mission in the minimum time possible while saving the maximum number of people. A novel technique for the SAR problem is proposed and referred to as the layered search and rescue (LSAR) algorithm. The novelty of LSAR involves simulating real disasters to distribute SAR tasks among UAVs. The performance of LSAR is compared, in terms of percentage of rescued survivors and rescue and execution times, with the max-sum, auction-based, and locust-inspired approaches for multi UAV task allocation (LIAM) and opportunistic task allocation (OTA) schemes. The simulation results show that the UAVs running the LSAR algorithm on average rescue approximately 74% of the survivors, which is 8% higher than the next best algorithm (LIAM). Moreover, this percentage increases with the number of UAVs, almost linearly with the least slope, which means more scalability and coverage is obtained in comparison to other algorithms. In addition, the empirical cumulative distribution function of LSAR results shows that the percentages of rescued survivors clustered around the [78% 100%] range under an exponential curve, meaning most results are above 50%. In comparison, all the other algorithms have almost equal distributions of their percentage of rescued survivor results. Furthermore, because the LSAR algorithm focuses on the center of the disaster, it nds more survivors and rescues them faster than the other algorithms, with an average of 55% 77%. Moreover, most registered times to rescue survivors by LSAR are bounded by a time of 04:50:02 with 95% con dence for a one-month mission time.IEEERepositório Científico do Instituto Politécnico do PortoAlotaibi, Ebtehal TurkiSaleh Alqefari, ShahadKoubaa, Anis2019-06-06T09:07:23Z20192019-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/13852eng2169-353610.1109/ACCESS.2019.2912306info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-03-13T12:56:11Zoai:recipp.ipp.pt:10400.22/13852Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:33:44.456906Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv LSAR: Multi-UAV Collaboration for Search and Rescue Missions
title LSAR: Multi-UAV Collaboration for Search and Rescue Missions
spellingShingle LSAR: Multi-UAV Collaboration for Search and Rescue Missions
Alotaibi, Ebtehal Turki
Autonomous agents
Drones
Search and rescue
Unmanned aerial vehicles
title_short LSAR: Multi-UAV Collaboration for Search and Rescue Missions
title_full LSAR: Multi-UAV Collaboration for Search and Rescue Missions
title_fullStr LSAR: Multi-UAV Collaboration for Search and Rescue Missions
title_full_unstemmed LSAR: Multi-UAV Collaboration for Search and Rescue Missions
title_sort LSAR: Multi-UAV Collaboration for Search and Rescue Missions
author Alotaibi, Ebtehal Turki
author_facet Alotaibi, Ebtehal Turki
Saleh Alqefari, Shahad
Koubaa, Anis
author_role author
author2 Saleh Alqefari, Shahad
Koubaa, Anis
author2_role author
author
dc.contributor.none.fl_str_mv Repositório Científico do Instituto Politécnico do Porto
dc.contributor.author.fl_str_mv Alotaibi, Ebtehal Turki
Saleh Alqefari, Shahad
Koubaa, Anis
dc.subject.por.fl_str_mv Autonomous agents
Drones
Search and rescue
Unmanned aerial vehicles
topic Autonomous agents
Drones
Search and rescue
Unmanned aerial vehicles
description In this paper, we consider the use of a team of multiple unmanned aerial vehicles (UAVs) to accomplish a search and rescue (SAR) mission in the minimum time possible while saving the maximum number of people. A novel technique for the SAR problem is proposed and referred to as the layered search and rescue (LSAR) algorithm. The novelty of LSAR involves simulating real disasters to distribute SAR tasks among UAVs. The performance of LSAR is compared, in terms of percentage of rescued survivors and rescue and execution times, with the max-sum, auction-based, and locust-inspired approaches for multi UAV task allocation (LIAM) and opportunistic task allocation (OTA) schemes. The simulation results show that the UAVs running the LSAR algorithm on average rescue approximately 74% of the survivors, which is 8% higher than the next best algorithm (LIAM). Moreover, this percentage increases with the number of UAVs, almost linearly with the least slope, which means more scalability and coverage is obtained in comparison to other algorithms. In addition, the empirical cumulative distribution function of LSAR results shows that the percentages of rescued survivors clustered around the [78% 100%] range under an exponential curve, meaning most results are above 50%. In comparison, all the other algorithms have almost equal distributions of their percentage of rescued survivor results. Furthermore, because the LSAR algorithm focuses on the center of the disaster, it nds more survivors and rescues them faster than the other algorithms, with an average of 55% 77%. Moreover, most registered times to rescue survivors by LSAR are bounded by a time of 04:50:02 with 95% con dence for a one-month mission time.
publishDate 2019
dc.date.none.fl_str_mv 2019-06-06T09:07:23Z
2019
2019-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.22/13852
url http://hdl.handle.net/10400.22/13852
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2169-3536
10.1109/ACCESS.2019.2912306
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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