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https://hdl.handle.net/11147/11804
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DC Field | Value | Language |
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dc.contributor.author | Sarkar, Mrinmoy | - |
dc.contributor.author | Yan, Xuyang | - |
dc.contributor.author | Erol, Berat Alper | - |
dc.contributor.author | Raptis, Ioannis | - |
dc.contributor.author | Homaifar, Abdollah | - |
dc.date.accessioned | 2021-12-02T18:16:13Z | - |
dc.date.available | 2021-12-02T18:16:13Z | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 0921-8890 | - |
dc.identifier.issn | 1872-793X | - |
dc.identifier.uri | https://doi.org/10.1016/j.robot.2021.103848 | - |
dc.identifier.uri | https://hdl.handle.net/11147/11804 | - |
dc.description.abstract | In 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.sponsorship | The 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.iso | en | en_US |
dc.publisher | Elsevier | en_US |
dc.relation.ispartof | Robotics and Autonomous Systems | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | UAV | en_US |
dc.subject | Multi-agent autonomous system | en_US |
dc.subject | AMASE | en_US |
dc.subject | Search & survey | en_US |
dc.subject | Collaborative operation | en_US |
dc.subject | Robotics | en_US |
dc.title | A novel search and survey technique for unmanned aerial systems in detecting and estimating the area for wildfires | en_US |
dc.type | Article | en_US |
dc.institutionauthor | Erol, Berat Alper | - |
dc.department | İzmir Institute of Technology. Computer Engineering | en_US |
dc.identifier.volume | 145 | en_US |
dc.identifier.wos | WOS:000709142300009 | en_US |
dc.identifier.scopus | 2-s2.0-85112553480 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.doi | 10.1016/j.robot.2021.103848 | - |
dc.identifier.wosquality | Q2 | - |
dc.identifier.scopusquality | Q1 | - |
item.openairetype | Article | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.fulltext | With Fulltext | - |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | open | - |
crisitem.author.dept | 03.04. Department of Computer Engineering | - |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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File | Size | Format | |
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1-s2.0-S0921889021001330-main.pdf | 1.58 MB | Adobe PDF | View/Open |
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