{"id":"https://openalex.org/W7141548553","doi":"https://doi.org/10.1109/tsusc.2026.3678502","title":"A Reinforcement Learning Framework for UAV Path Planning Automation in Complex Fire and Rescue Environments","display_name":"A Reinforcement Learning Framework for UAV Path Planning Automation in Complex Fire and Rescue Environments","publication_year":2026,"publication_date":"2026-03-27","ids":{"openalex":"https://openalex.org/W7141548553","doi":"https://doi.org/10.1109/tsusc.2026.3678502"},"language":null,"primary_location":{"id":"doi:10.1109/tsusc.2026.3678502","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsusc.2026.3678502","pdf_url":null,"source":{"id":"https://openalex.org/S4210221417","display_name":"IEEE Transactions on Sustainable Computing","issn_l":"2377-3782","issn":["2377-3782","2377-3790"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Sustainable Computing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130762284","display_name":"Yanming Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yanming Chen","raw_affiliation_strings":["Department of Biostatistics, Graduate School of Arts and Sciences, Yale University, New Haven, CT, USA"],"raw_orcid":"https://orcid.org/0009-0004-2732-474X","affiliations":[{"raw_affiliation_string":"Department of Biostatistics, Graduate School of Arts and Sciences, Yale University, New Haven, CT, USA","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026023968","display_name":"Saeid Pourroostaei Ardakani","orcid":"https://orcid.org/0000-0001-8062-6617"},"institutions":[{"id":"https://openalex.org/I166337079","display_name":"Queen Mary University of London","ror":"https://ror.org/026zzn846","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I166337079"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Saeid Pourroostaei Ardakani","raw_affiliation_strings":["School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK"],"raw_orcid":"https://orcid.org/0000-0001-8062-6617","affiliations":[{"raw_affiliation_string":"School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK","institution_ids":["https://openalex.org/I166337079"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.31130458,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"11","issue":"3","first_page":"266","last_page":"277"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10586","display_name":"Robotic Path Planning Algorithms","score":0.8198999762535095,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10586","display_name":"Robotic Path Planning Algorithms","score":0.8198999762535095,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10879","display_name":"Robotic Locomotion and Control","score":0.021800000220537186,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.019099999219179153,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/automation","display_name":"Automation","score":0.5871999859809875},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.569599986076355},{"id":"https://openalex.org/keywords/motion-planning","display_name":"Motion planning","score":0.5604000091552734},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.421999990940094},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.3601999878883362},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.29600000381469727},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.2957000136375427}],"concepts":[{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.5871999859809875},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.569599986076355},{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.5604000091552734},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.535099983215332},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.421999990940094},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.4027000069618225},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.3601999878883362},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3499000072479248},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.29600000381469727},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.2957000136375427},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.29420000314712524},{"id":"https://openalex.org/C133731056","wikidata":"https://www.wikidata.org/wiki/Q4917288","display_name":"Control engineering","level":1,"score":0.27649998664855957},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2761000096797943},{"id":"https://openalex.org/C21457203","wikidata":"https://www.wikidata.org/wiki/Q4056293","display_name":"Process automation system","level":3,"score":0.2694999873638153},{"id":"https://openalex.org/C19966478","wikidata":"https://www.wikidata.org/wiki/Q4810574","display_name":"Mobile robot","level":3,"score":0.26899999380111694},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.26669999957084656},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.2587999999523163},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.2531999945640564},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.2506999969482422}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tsusc.2026.3678502","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsusc.2026.3678502","pdf_url":null,"source":{"id":"https://openalex.org/S4210221417","display_name":"IEEE Transactions on Sustainable Computing","issn_l":"2377-3782","issn":["2377-3782","2377-3790"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Sustainable Computing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Climate action","score":0.418961763381958,"id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Fire":[0],"and":[1,15,26,42,64,70,84,97,114,134,139],"rescue":[2,21,61,71,135],"UAVs":[3,39],"employ":[4],"AI":[5],"methods":[6],"to":[7,13,40,67,95],"automate":[8],"navigation":[9],"over":[10],"fire":[11,17,44,69,85,133],"zones":[12],"detect":[14,41],"extinguish":[16],"f":[18],"lames":[19],"and/or":[20],"fire-trapped":[22],"lives":[23],"through":[24],"low-cost":[25],"collision-free":[27],"flight":[28,65],"routes.":[29],"This":[30],"paper":[31],"introduces":[32],"a":[33],"reinforcement":[34],"learning":[35],"framework":[36],"that":[37,120],"allows":[38],"prioritise":[43],"trapped":[45,82],"targets":[46],"based":[47],"on":[48],"their":[49],"risk":[50],"levels.":[51],"It":[52],"establishes":[53],"three":[54,105],"simultaneous":[55],"objectives,":[56],"including":[57,108],"maximised":[58],"power":[59],"conservation,":[60],"success":[62],"rate,":[63],"safety,":[66],"plan":[68],"operations":[72],"in":[73,127],"complex":[74],"environments":[75],"where":[76],"field":[77],"size,":[78],"the":[79,99,102,121,125,132],"number":[80],"of":[81,101],"targets,":[83],"source":[86],"count":[87],"vary.":[88],"An":[89],"extensive":[90],"empirical":[91],"evaluation":[92],"is":[93,137],"conducted":[94],"test":[96],"evaluate":[98],"performance":[100],"proposal":[103],"against":[104],"well-known":[106],"benchmarks,":[107],"Double":[109],"Deep":[110],"Q-network,":[111],"Advantage":[112],"Actor-Critic,":[113],"Genetic":[115],"Algorithm.":[116],"The":[117],"results":[118],"demonstrate":[119],"proposed":[122],"solution":[123],"outperforms":[124],"benchmarks":[126],"most":[128],"circumstances,":[129],"especially":[130],"when":[131],"environment":[136],"large":[138],"complex.":[140]},"counts_by_year":[],"updated_date":"2026-06-06T06:22:57.294733","created_date":"2026-03-28T00:00:00"}