Wildfire detection in large-scale environments using force-based control for swarms of UAVs

Autores
Tzoumas, Georgios; Pitonakova, Lenka; Salinas, Lucio Rafael; Scales, Charles; Richardson, Thomas; Hauert, Sabine
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Wildfres afect countries worldwide as global warming increases the probability of their appearance. Monitoring vast areas of forests can be challenging due to the lack of resources and information. Additionally, early detection of wildfres can be benefcial for their mitigation. To this end, we explore in simulation the use of swarms of uncrewed aerial vehicles (UAVs) with long autonomy that can cover large areas the size of California to detect early stage wildfres. Four decentralised control algorithms are tested: (1) random walking, (2) dispersion, (3) pheromone avoidance and (4) dynamic space partition. The frst three adaptations are known from literature, whereas the last one is newly developed. The algorithms are tested with swarms of diferent sizes to test the spatial coverage of the system in 24 h of simulation time. Best results are achieved using a version of the dynamic space partition algorithm (DSP) which can detect 82% of the fres using only 20 UAVs. When the swarm consists of 40 or more aircraft 100% coverage can also be achieved. Further tests of DSP show robustness when agents fail and when new fres are generated in the area.
Fil: Tzoumas, Georgios. University of Bristol; Reino Unido. Bristol Robotics Laboratory; Reino Unido
Fil: Pitonakova, Lenka. University of Bristol; Reino Unido. Windracers Ltd; Reino Unido
Fil: Salinas, Lucio Rafael. University of Bristol; Reino Unido. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Bristol Robotics Laboratory; Reino Unido
Fil: Scales, Charles. Windracers Ltd; Reino Unido
Fil: Richardson, Thomas. University of Bristol; Reino Unido. Bristol Robotics Laboratory; Reino Unido
Fil: Hauert, Sabine. University of Bristol; Reino Unido. Bristol Robotics Laboratory; Reino Unido
Materia
DYNAMIC SPACE PARTITION
MONITORING
PHYSICOMIMETICS
SWARMS
UAVS
WILDFIRES
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/227832
Acceder
id CONICETDig_ff3c29b547dd881d59013dcd79440559
oai_identifier_str oai:ri.conicet.gov.ar:11336/227832
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Wildfire detection in large-scale environments using force-based control for swarms of UAVsTzoumas, GeorgiosPitonakova, LenkaSalinas, Lucio RafaelScales, CharlesRichardson, ThomasHauert, SabineDYNAMIC SPACE PARTITIONMONITORINGPHYSICOMIMETICSSWARMSUAVSWILDFIREShttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2https://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1https://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1Wildfres afect countries worldwide as global warming increases the probability of their appearance. Monitoring vast areas of forests can be challenging due to the lack of resources and information. Additionally, early detection of wildfres can be benefcial for their mitigation. To this end, we explore in simulation the use of swarms of uncrewed aerial vehicles (UAVs) with long autonomy that can cover large areas the size of California to detect early stage wildfres. Four decentralised control algorithms are tested: (1) random walking, (2) dispersion, (3) pheromone avoidance and (4) dynamic space partition. The frst three adaptations are known from literature, whereas the last one is newly developed. The algorithms are tested with swarms of diferent sizes to test the spatial coverage of the system in 24 h of simulation time. Best results are achieved using a version of the dynamic space partition algorithm (DSP) which can detect 82% of the fres using only 20 UAVs. When the swarm consists of 40 or more aircraft 100% coverage can also be achieved. Further tests of DSP show robustness when agents fail and when new fres are generated in the area.Fil: Tzoumas, Georgios. University of Bristol; Reino Unido. Bristol Robotics Laboratory; Reino UnidoFil: Pitonakova, Lenka. University of Bristol; Reino Unido. Windracers Ltd; Reino UnidoFil: Salinas, Lucio Rafael. University of Bristol; Reino Unido. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Bristol Robotics Laboratory; Reino UnidoFil: Scales, Charles. Windracers Ltd; Reino UnidoFil: Richardson, Thomas. University of Bristol; Reino Unido. Bristol Robotics Laboratory; Reino UnidoFil: Hauert, Sabine. University of Bristol; Reino Unido. Bristol Robotics Laboratory; Reino UnidoSpringer2023-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/227832Tzoumas, Georgios; Pitonakova, Lenka; Salinas, Lucio Rafael; Scales, Charles; Richardson, Thomas; et al.; Wildfire detection in large-scale environments using force-based control for swarms of UAVs; Springer; Swarm Intelligence; 17; 1-2; 2-2023; 89-1151935-38121935-3820CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1007/s11721-022-00218-9info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007/s11721-022-00218-9info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:02:01Zoai:ri.conicet.gov.ar:11336/227832instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-22 11:02:02.025CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Wildfire detection in large-scale environments using force-based control for swarms of UAVs
title Wildfire detection in large-scale environments using force-based control for swarms of UAVs
spellingShingle Wildfire detection in large-scale environments using force-based control for swarms of UAVs
Tzoumas, Georgios
DYNAMIC SPACE PARTITION
MONITORING
PHYSICOMIMETICS
SWARMS
UAVS
WILDFIRES
title_short Wildfire detection in large-scale environments using force-based control for swarms of UAVs
title_full Wildfire detection in large-scale environments using force-based control for swarms of UAVs
title_fullStr Wildfire detection in large-scale environments using force-based control for swarms of UAVs
title_full_unstemmed Wildfire detection in large-scale environments using force-based control for swarms of UAVs
title_sort Wildfire detection in large-scale environments using force-based control for swarms of UAVs
dc.creator.none.fl_str_mv Tzoumas, Georgios
Pitonakova, Lenka
Salinas, Lucio Rafael
Scales, Charles
Richardson, Thomas
Hauert, Sabine
author Tzoumas, Georgios
author_facet Tzoumas, Georgios
Pitonakova, Lenka
Salinas, Lucio Rafael
Scales, Charles
Richardson, Thomas
Hauert, Sabine
author_role author
author2 Pitonakova, Lenka
Salinas, Lucio Rafael
Scales, Charles
Richardson, Thomas
Hauert, Sabine
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv DYNAMIC SPACE PARTITION
MONITORING
PHYSICOMIMETICS
SWARMS
UAVS
WILDFIRES
topic DYNAMIC SPACE PARTITION
MONITORING
PHYSICOMIMETICS
SWARMS
UAVS
WILDFIRES
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Wildfres afect countries worldwide as global warming increases the probability of their appearance. Monitoring vast areas of forests can be challenging due to the lack of resources and information. Additionally, early detection of wildfres can be benefcial for their mitigation. To this end, we explore in simulation the use of swarms of uncrewed aerial vehicles (UAVs) with long autonomy that can cover large areas the size of California to detect early stage wildfres. Four decentralised control algorithms are tested: (1) random walking, (2) dispersion, (3) pheromone avoidance and (4) dynamic space partition. The frst three adaptations are known from literature, whereas the last one is newly developed. The algorithms are tested with swarms of diferent sizes to test the spatial coverage of the system in 24 h of simulation time. Best results are achieved using a version of the dynamic space partition algorithm (DSP) which can detect 82% of the fres using only 20 UAVs. When the swarm consists of 40 or more aircraft 100% coverage can also be achieved. Further tests of DSP show robustness when agents fail and when new fres are generated in the area.
Fil: Tzoumas, Georgios. University of Bristol; Reino Unido. Bristol Robotics Laboratory; Reino Unido
Fil: Pitonakova, Lenka. University of Bristol; Reino Unido. Windracers Ltd; Reino Unido
Fil: Salinas, Lucio Rafael. University of Bristol; Reino Unido. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Bristol Robotics Laboratory; Reino Unido
Fil: Scales, Charles. Windracers Ltd; Reino Unido
Fil: Richardson, Thomas. University of Bristol; Reino Unido. Bristol Robotics Laboratory; Reino Unido
Fil: Hauert, Sabine. University of Bristol; Reino Unido. Bristol Robotics Laboratory; Reino Unido
description Wildfres afect countries worldwide as global warming increases the probability of their appearance. Monitoring vast areas of forests can be challenging due to the lack of resources and information. Additionally, early detection of wildfres can be benefcial for their mitigation. To this end, we explore in simulation the use of swarms of uncrewed aerial vehicles (UAVs) with long autonomy that can cover large areas the size of California to detect early stage wildfres. Four decentralised control algorithms are tested: (1) random walking, (2) dispersion, (3) pheromone avoidance and (4) dynamic space partition. The frst three adaptations are known from literature, whereas the last one is newly developed. The algorithms are tested with swarms of diferent sizes to test the spatial coverage of the system in 24 h of simulation time. Best results are achieved using a version of the dynamic space partition algorithm (DSP) which can detect 82% of the fres using only 20 UAVs. When the swarm consists of 40 or more aircraft 100% coverage can also be achieved. Further tests of DSP show robustness when agents fail and when new fres are generated in the area.
publishDate 2023
dc.date.none.fl_str_mv 2023-02
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/227832
Tzoumas, Georgios; Pitonakova, Lenka; Salinas, Lucio Rafael; Scales, Charles; Richardson, Thomas; et al.; Wildfire detection in large-scale environments using force-based control for swarms of UAVs; Springer; Swarm Intelligence; 17; 1-2; 2-2023; 89-115
1935-3812
1935-3820
CONICET Digital
CONICET
url http://hdl.handle.net/11336/227832
identifier_str_mv Tzoumas, Georgios; Pitonakova, Lenka; Salinas, Lucio Rafael; Scales, Charles; Richardson, Thomas; et al.; Wildfire detection in large-scale environments using force-based control for swarms of UAVs; Springer; Swarm Intelligence; 17; 1-2; 2-2023; 89-115
1935-3812
1935-3820
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1007/s11721-022-00218-9
info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007/s11721-022-00218-9
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1846781218679422976
score 12.982451

Publicaciones similares