Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/11804
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSarkar, Mrinmoy-
dc.contributor.authorYan, Xuyang-
dc.contributor.authorErol, Berat Alper-
dc.contributor.authorRaptis, Ioannis-
dc.contributor.authorHomaifar, Abdollah-
dc.date.accessioned2021-12-02T18:16:13Z-
dc.date.available2021-12-02T18:16:13Z-
dc.date.issued2021-
dc.identifier.issn0921-8890-
dc.identifier.issn1872-793X-
dc.identifier.urihttps://doi.org/10.1016/j.robot.2021.103848-
dc.identifier.urihttps://hdl.handle.net/11147/11804-
dc.description.abstractIn recent years Unmanned Aerial Vehicles (UAVs) have progressively been utilized for wildfire management, and are especially in prevalent in forest fire monitoring missions. To ensure the fast detection and accurate area estimation of forest fires, a two-step search and survey algorithm for multi-UAV system is proposed to address these fire scenarios. Initially, a grid-based partition method is applied to divide the area-of-interest into several search areas. Then, an archetype search pattern is used to provide timely UAV exploration within those sub-areas. Once the fire zones are detected, a novel survey strategy is employed for UAVs to discover the boundary points of the fire zones, so that the area of the fire zones can be estimated using the sampled boundary points. In addition, the effect of wind is accounted for improving fire zone boundary estimates. The proposed search-and-survey procedure is validated on multiple simulated scenarios using the U.S. Air Force's mission-realistic Aerospace Multi-Agent Simulation Environment (AMASE) software. Simulation results showcase that the proposed search pattern can effectively discover the seeded fire zones within 40 min of the mission. This is relatively faster than the other two well-known search patterns. Moreover, the proposed survey technique provides a coverage estimate with at least 85% accuracy for the area of interest within 90 min of the mission. (C) 2021 Elsevier B.V. All rights reserved.en_US
dc.description.sponsorshipThe authors would like to thank TECHLAV, United States of America for the financial support under the agreement number FA8750-15-2-0116 and the U.S. Air Force Research Laboratory for allowing to use their AMASE software before, during and after the ``Swarm and Search AI Challenge: 2019 Fire Hackevent, which was hosted by the Wright Brothers Institute and University of Dayton Research Institute. Also, this work is partially funded through the National Institute of Aerospace's Langley Distinguished Professor Program, United States of America under grant number C16-2B00-NCAT. The authors would also like to thank the NASA Langley Research Center, United States of America and the NASA University Leadership Initiative (ULI), United States of America under agreement number 2 CFR 200.514.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofRobotics and Autonomous Systemsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectUAVen_US
dc.subjectMulti-agent autonomous systemen_US
dc.subjectAMASEen_US
dc.subjectSearch & surveyen_US
dc.subjectCollaborative operationen_US
dc.subjectRoboticsen_US
dc.titleA novel search and survey technique for unmanned aerial systems in detecting and estimating the area for wildfiresen_US
dc.typeArticleen_US
dc.institutionauthorErol, Berat Alper-
dc.departmentİzmir Institute of Technology. Computer Engineeringen_US
dc.identifier.volume145en_US
dc.identifier.wosWOS:000709142300009en_US
dc.identifier.scopus2-s2.0-85112553480en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1016/j.robot.2021.103848-
dc.identifier.wosqualityQ2-
dc.identifier.scopusqualityQ1-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypeArticle-
item.fulltextWith Fulltext-
crisitem.author.dept03.04. Department of Computer Engineering-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Files in This Item:
File SizeFormat 
1-s2.0-S0921889021001330-main.pdf1.58 MBAdobe PDFView/Open
Show simple item record



CORE Recommender

SCOPUSTM   
Citations

7
checked on Jun 21, 2024

WEB OF SCIENCETM
Citations

5
checked on Jun 15, 2024

Page view(s)

102
checked on Jun 24, 2024

Download(s)

26
checked on Jun 24, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.