{"id":"https://openalex.org/W3090907972","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207490","title":"Deep Reinforcement Learning for Motion Planning of Quadrotors Using Raw Depth Images","display_name":"Deep Reinforcement Learning for Motion Planning of Quadrotors Using Raw Depth Images","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3090907972","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207490","mag":"3090907972"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn48605.2020.9207490","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207490","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","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/A5016173969","display_name":"Efe Camci","orcid":"https://orcid.org/0000-0002-5342-5163"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":true,"raw_author_name":"Efe Camci","raw_affiliation_strings":["School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore"],"affiliations":[{"raw_affiliation_string":"School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079258091","display_name":"Domenico Campolo","orcid":"https://orcid.org/0000-0001-6930-0413"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Domenico Campolo","raw_affiliation_strings":["School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore"],"affiliations":[{"raw_affiliation_string":"School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068099488","display_name":"Erdal Kayacan","orcid":"https://orcid.org/0000-0002-7143-8777"},"institutions":[{"id":"https://openalex.org/I204337017","display_name":"Aarhus University","ror":"https://ror.org/01aj84f44","country_code":"DK","type":"education","lineage":["https://openalex.org/I204337017"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Erdal Kayacan","raw_affiliation_strings":["Department of Engineering, Aarhus University, Aarhus C, Denmark"],"affiliations":[{"raw_affiliation_string":"Department of Engineering, Aarhus University, Aarhus C, Denmark","institution_ids":["https://openalex.org/I204337017"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5016173969"],"corresponding_institution_ids":["https://openalex.org/I172675005"],"apc_list":null,"apc_paid":null,"fwci":0.9828,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.78640051,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10586","display_name":"Robotic Path Planning Algorithms","score":0.9997000098228455,"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":0.9997000098228455,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.991599977016449,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9915000200271606,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8065081834793091},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7143648266792297},{"id":"https://openalex.org/keywords/motion-planning","display_name":"Motion planning","score":0.700731635093689},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6813493967056274},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5413349866867065},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.5117425918579102},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.4896572232246399},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.4678175151348114},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.3786596655845642}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8065081834793091},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7143648266792297},{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.700731635093689},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6813493967056274},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5413349866867065},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.5117425918579102},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.4896572232246399},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.4678175151348114},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.3786596655845642},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ijcnn48605.2020.9207490","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207490","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.atira.dk:publications/a238052a-a375-4775-9aef-4e33a60fc145","is_oa":false,"landing_page_url":"https://pure.au.dk/portal/en/publications/a238052a-a375-4775-9aef-4e33a60fc145","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Camci, E, Campolo, D & Kayacan, E 2020, Deep Reinforcement Learning for Motion Planning of Quadrotors Using Raw Depth Images. in 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings., 9207490, IEEE, 2020 International Joint Conference on Neural Networks, IJCNN 2020, Virtual, Glasgow, United Kingdom, 19/07/2020. https://doi.org/10.1109/IJCNN48605.2020.9207490","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W2046033161","https://openalex.org/W2103120971","https://openalex.org/W2104332709","https://openalex.org/W2121863487","https://openalex.org/W2145339207","https://openalex.org/W2158955701","https://openalex.org/W2162991084","https://openalex.org/W2509493982","https://openalex.org/W2615547864","https://openalex.org/W2739434881","https://openalex.org/W2761373498","https://openalex.org/W2794268423","https://openalex.org/W2883162277","https://openalex.org/W2884740144","https://openalex.org/W2891701184","https://openalex.org/W2909708126","https://openalex.org/W2910288398","https://openalex.org/W2962957005","https://openalex.org/W2962987986","https://openalex.org/W2963451844","https://openalex.org/W2963552895","https://openalex.org/W2964121744","https://openalex.org/W2972816955","https://openalex.org/W2975532148","https://openalex.org/W2977843878","https://openalex.org/W3003268826","https://openalex.org/W3098035776","https://openalex.org/W3106333735","https://openalex.org/W4214717370","https://openalex.org/W6631190155","https://openalex.org/W6731094094","https://openalex.org/W6761450092","https://openalex.org/W6768067086"],"related_works":["https://openalex.org/W17155033","https://openalex.org/W3207760230","https://openalex.org/W2359600231","https://openalex.org/W2380019117","https://openalex.org/W3138952546","https://openalex.org/W1987886368","https://openalex.org/W1660309994","https://openalex.org/W2369187583","https://openalex.org/W3197207153","https://openalex.org/W2134215012"],"abstract_inverted_index":{"In":[0],"this":[1],"work,":[2],"we":[3],"introduce":[4],"a":[5,15,35],"novel,":[6],"end-to-end":[7],"motion":[8,28,51],"planner":[9],"for":[10,104],"quadrotor":[11],"navigation.":[12],"Informed":[13],"by":[14],"rough":[16],"path":[17],"to":[18,48],"goal":[19],"in":[20,77,85,129],"partially":[21],"unknown":[22],"environments,":[23],"our":[24,63,102],"method":[25,64,81,103,123],"creates":[26],"desirable":[27,50],"plans":[29],"using":[30],"raw":[31],"depth":[32],"images":[33,47],"from":[34],"front-facing":[36],"camera.":[37],"It":[38],"exploits":[39],"correlations":[40],"between":[41],"local":[42],"spatial":[43],"portions":[44],"of":[45,87],"these":[46],"generate":[49],"primitive":[52],"sequences":[53],"on":[54],"the":[55],"fly":[56],"without":[57],"conducting":[58],"explicit":[59],"sensing-reconstructing-planning.":[60],"We":[61,99],"evaluate":[62],"through":[65],"an":[66],"extensive":[67],"comparison":[68],"with":[69,108,113,134],"three":[70,130],"competitor":[71],"algorithms":[72],"over":[73,96],"ten":[74],"different":[75,131],"environments":[76],"AirSim":[78],"simulations.":[79],"Our":[80,122],"outperforms":[82],"its":[83,119],"competitors":[84],"terms":[86],"safe":[88],"navigation":[89,91],"distance,":[90],"time,":[92],"and":[93,117],"crash":[94],"rate":[95],"50":[97],"flights.":[98],"also":[100],"deploy":[101],"real":[105,127],"flight":[106],"tests":[107],"DJI":[109],"F330":[110],"Quadrotor":[111],"equipped":[112],"Intel":[114],"RealSense":[115],"D435,":[116],"demonstrate":[118],"real-time":[120],"ap-plicability.":[121],"successfully":[124],"performs":[125],"15":[126],"flights":[128],"environment":[132],"settings":[133],"increasing":[135],"complexity.":[136],"The":[137],"experiments":[138],"can":[139],"be":[140],"found":[141],"at":[142],"https://youtu.be/hw0sxNwliqs.":[143]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":2}],"updated_date":"2026-03-13T16:22:10.518609","created_date":"2025-10-10T00:00:00"}
