{"id":"https://openalex.org/W3093822448","doi":"https://doi.org/10.1109/mlsp49062.2020.9231577","title":"Collision-Free UAV Navigation with a Monocular Camera Using Deep Reinforcement Learning","display_name":"Collision-Free UAV Navigation with a Monocular Camera Using Deep Reinforcement Learning","publication_year":2020,"publication_date":"2020-09-01","ids":{"openalex":"https://openalex.org/W3093822448","doi":"https://doi.org/10.1109/mlsp49062.2020.9231577","mag":"3093822448"},"language":"en","primary_location":{"id":"doi:10.1109/mlsp49062.2020.9231577","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp49062.2020.9231577","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-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/A5100416532","display_name":"Yun Chen","orcid":"https://orcid.org/0000-0002-2338-6909"},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yun Chen","raw_affiliation_strings":["USA Department of Electrical and Computer Engineering, The University of Texas, Austin"],"affiliations":[{"raw_affiliation_string":"USA Department of Electrical and Computer Engineering, The University of Texas, Austin","institution_ids":["https://openalex.org/I86519309"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056278281","display_name":"Nuria Gonz\u00e1lez\u2010Prelcic","orcid":"https://orcid.org/0000-0002-0828-8454"},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nuria Gonzalez-Prelcic","raw_affiliation_strings":["USA Department of Electrical and Computer Engineering, The University of Texas, Austin"],"affiliations":[{"raw_affiliation_string":"USA Department of Electrical and Computer Engineering, The University of Texas, Austin","institution_ids":["https://openalex.org/I86519309"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029812222","display_name":"Robert W. Heath","orcid":"https://orcid.org/0000-0002-4666-5628"},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Robert W. Heath","raw_affiliation_strings":["USA Department of Electrical and Computer Engineering, The University of Texas, Austin"],"affiliations":[{"raw_affiliation_string":"USA Department of Electrical and Computer Engineering, The University of Texas, Austin","institution_ids":["https://openalex.org/I86519309"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100416532"],"corresponding_institution_ids":["https://openalex.org/I86519309"],"apc_list":null,"apc_paid":null,"fwci":26.0785,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":{"value":0.99063246,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11133","display_name":"UAV Applications and Optimization","score":0.9993000030517578,"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"}},"topics":[{"id":"https://openalex.org/T11133","display_name":"UAV Applications and Optimization","score":0.9993000030517578,"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"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9979000091552734,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9972000122070312,"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/computer-science","display_name":"Computer science","score":0.7471026182174683},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.7153942584991455},{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.6613826751708984},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6505374908447266},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6402409672737122},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.598840594291687},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.572472333908081},{"id":"https://openalex.org/keywords/collision-avoidance","display_name":"Collision avoidance","score":0.5552425980567932},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.547077476978302},{"id":"https://openalex.org/keywords/collision","display_name":"Collision","score":0.4809421896934509},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.45307445526123047},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4511692225933075},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4162839651107788},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.10433617234230042},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.0943731963634491},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.08097589015960693}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7471026182174683},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7153942584991455},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.6613826751708984},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6505374908447266},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6402409672737122},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.598840594291687},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.572472333908081},{"id":"https://openalex.org/C2780864053","wikidata":"https://www.wikidata.org/wiki/Q5147495","display_name":"Collision avoidance","level":3,"score":0.5552425980567932},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.547077476978302},{"id":"https://openalex.org/C121704057","wikidata":"https://www.wikidata.org/wiki/Q352070","display_name":"Collision","level":2,"score":0.4809421896934509},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.45307445526123047},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4511692225933075},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4162839651107788},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.10433617234230042},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0943731963634491},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.08097589015960693},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mlsp49062.2020.9231577","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp49062.2020.9231577","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W32403112","https://openalex.org/W1634692259","https://openalex.org/W1757796397","https://openalex.org/W2006433700","https://openalex.org/W2395047452","https://openalex.org/W2440082530","https://openalex.org/W2462964962","https://openalex.org/W2557728737","https://openalex.org/W2582222835","https://openalex.org/W2603800188","https://openalex.org/W2609009256","https://openalex.org/W2788239209","https://openalex.org/W2799134332","https://openalex.org/W2891381456","https://openalex.org/W2962957005","https://openalex.org/W2963163009","https://openalex.org/W2963544079","https://openalex.org/W4213147678","https://openalex.org/W4298857966"],"related_works":["https://openalex.org/W4317634134","https://openalex.org/W2889566344","https://openalex.org/W2981729160","https://openalex.org/W2743212448","https://openalex.org/W4362501864","https://openalex.org/W650625605","https://openalex.org/W4306904969","https://openalex.org/W4310743282","https://openalex.org/W1819938260","https://openalex.org/W2340892746"],"abstract_inverted_index":{"Small":[0],"unmanned":[1],"aerial":[2],"vehicles":[3],"(UAV)":[4],"with":[5],"reduced":[6],"sensing":[7,61],"and":[8,53,83,92,140],"communication":[9],"capabilities":[10],"can":[11],"support":[12],"potential":[13],"use":[14],"cases":[15],"in":[16],"different":[17],"indoor":[18],"environments":[19],"such":[20],"as":[21],"automated":[22],"factories":[23],"or":[24],"commercial":[25],"buildings.":[26],"In":[27],"this":[28],"context,":[29],"we":[30],"consider":[31],"the":[32,70,86,102,108,119],"problem":[33],"of":[34,88,104,144],"collision-free":[35],"autonomous":[36],"UAV":[37],"navigation":[38,47],"supported":[39],"by":[40,64,138],"a":[41,46,65],"simple":[42],"sensor.":[43],"We":[44],"propose":[45],"system":[48],"based":[49],"on":[50,69],"object":[51,96],"detection":[52,73,97],"deep":[54,109],"reinforcement":[55],"learning":[56],"(DRL)":[57],"that":[58],"only":[59,123],"exploits":[60],"data":[62],"obtained":[63],"monocular":[66],"camera":[67],"mounted":[68],"UAV.":[71],"Object":[72],"is":[74],"incorporated":[75],"into":[76],"DRL":[77],"training":[78],"to":[79,84,100,113,125],"reduce":[80],"flight":[81],"time":[82],"maximize":[85],"probability":[87],"avoiding":[89],"both":[90],"current":[91],"future":[93],"crashes.":[94],"Moreover,":[95],"also":[98,131],"helps":[99],"remove":[101],"impact":[103],"wrong":[105],"predictions":[106],"from":[107],"network.":[110],"When":[111],"compared":[112],"schemes":[114],"using":[115],"traditional":[116],"RL":[117],"methods,":[118],"proposed":[120],"framework":[121],"not":[122],"leads":[124],"collision-":[126],"free":[127],"trips,":[128],"but":[129],"it":[130],"reduces":[132],"flying":[133],"times":[134],"towards":[135],"given":[136],"destinations":[137],"25%,":[139],"cuts":[141],"down":[142],"50%":[143],"unnecessary":[145],"turns.":[146]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1}],"updated_date":"2026-04-03T22:45:19.894376","created_date":"2025-10-10T00:00:00"}
