LSAR: Multi-UAV Collaboration for Search and Rescue Missions
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , |
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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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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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 instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
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 |
repository.mail.fl_str_mv |
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1799131429507956736 |