{"id":"https://openalex.org/W4409868798","doi":"https://doi.org/10.1145/3718751.3718822","title":"Real-time path planning of general aircraft based on deep reinforcement learning","display_name":"Real-time path planning of general aircraft based on deep reinforcement learning","publication_year":2024,"publication_date":"2024-11-15","ids":{"openalex":"https://openalex.org/W4409868798","doi":"https://doi.org/10.1145/3718751.3718822"},"language":"en","primary_location":{"id":"doi:10.1145/3718751.3718822","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3718751.3718822","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 4th International Conference on Big Data, Artificial Intelligence and Risk Management","raw_type":"proceedings-article"},"type":"conference-paper","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/A5103609925","display_name":"Changqi Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I58995867","display_name":"Civil Aviation Flight University of China","ror":"https://ror.org/01xyb1v19","country_code":"CN","type":"education","lineage":["https://openalex.org/I58995867"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changqi Yang","raw_affiliation_strings":["College of Air Traffic Management, Civil Aviation Flight University of China, Guanghan, Sichuan, China"],"raw_orcid":"https://orcid.org/0009-0001-5260-3239","affiliations":[{"raw_affiliation_string":"College of Air Traffic Management, Civil Aviation Flight University of China, Guanghan, Sichuan, China","institution_ids":["https://openalex.org/I58995867"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009136593","display_name":"X. Cheng","orcid":null},"institutions":[{"id":"https://openalex.org/I58995867","display_name":"Civil Aviation Flight University of China","ror":"https://ror.org/01xyb1v19","country_code":"CN","type":"education","lineage":["https://openalex.org/I58995867"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangrui Cheng","raw_affiliation_strings":["College of Air Traffic Management, Civil Aviation Flight University of China, Guanghan, Sichuan, China"],"raw_orcid":"https://orcid.org/0009-0009-3582-9103","affiliations":[{"raw_affiliation_string":"College of Air Traffic Management, Civil Aviation Flight University of China, Guanghan, Sichuan, China","institution_ids":["https://openalex.org/I58995867"]}]},{"author_position":"last","author":{"id":null,"display_name":"Hongjie Hu","orcid":"https://orcid.org/0009-0007-6038-9731"},"institutions":[{"id":"https://openalex.org/I58995867","display_name":"Civil Aviation Flight University of China","ror":"https://ror.org/01xyb1v19","country_code":"CN","type":"education","lineage":["https://openalex.org/I58995867"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongjie Hu","raw_affiliation_strings":["College of Air Traffic Management, Civil Aviation Flight University of China, Guanghan, Sichuan, China"],"raw_orcid":"https://orcid.org/0009-0007-6038-9731","affiliations":[{"raw_affiliation_string":"College of Air Traffic Management, Civil Aviation Flight University of China, Guanghan, Sichuan, China","institution_ids":["https://openalex.org/I58995867"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I58995867"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"445","last_page":"450"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10586","display_name":"Robotic Path Planning Algorithms","score":0.9970999956130981,"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":0.9970999956130981,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9909999966621399,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/T11814","display_name":"Advanced Manufacturing and Logistics Optimization","score":0.9843999743461609,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/reinforcement-learning","display_name":"Reinforcement learning","score":0.7801406383514404},{"id":"https://openalex.org/keywords/motion-planning","display_name":"Motion planning","score":0.6733389496803284},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6690315008163452},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.5601924657821655},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5011487007141113},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.35089588165283203},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.0959453284740448},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.08929431438446045}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7801406383514404},{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.6733389496803284},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6690315008163452},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.5601924657821655},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5011487007141113},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.35089588165283203},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0959453284740448},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.08929431438446045}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3718751.3718822","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3718751.3718822","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 4th International Conference on Big Data, Artificial Intelligence and Risk Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":2,"referenced_works":["https://openalex.org/W1977357978","https://openalex.org/W4286615890"],"related_works":["https://openalex.org/W4306904969","https://openalex.org/W2138720691","https://openalex.org/W4362501864","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"],"abstract_inverted_index":{"In":[0,94],"recent":[1],"years,":[2],"with":[3,66],"the":[4,9,24,31,52,61,83,97,121,126,131,137,154,161,166,175,182,192,195,198,202,209],"development":[5],"of":[6,27,34,43,55,128,134,140,171,177,186,194,201],"general":[7,13,35,56],"aviation,":[8,45],"transportation":[10,53],"demand":[11],"for":[12,51],"aircraft":[14,37],"between":[15],"adjacent":[16],"cities":[17],"has":[18,38],"increased":[19],"greatly.":[20],"However,":[21],"due":[22],"to":[23,104,108,119,160],"airspace":[25],"complexity":[26],"low-altitude":[28],"airspace,":[29],"and":[30,69,111,124,143,165,174,184,188,208,212],"flight":[32],"path":[33,76,172,211],"aviation":[36,57],"greater":[39],"freedom":[40],"than":[41],"that":[42,153],"civil":[44],"which":[46,180],"puts":[47],"forward":[48],"higher":[49,106],"requirements":[50],"safety":[54,185],"transportation.":[58,189],"Based":[59],"on":[60,72,82],"deep":[62],"reinforcement":[63],"learning":[64,113,116],"algorithm":[65,157,164,168,205],"good":[67],"generalization":[68],"weak":[70],"dependence":[71],"environment,":[73],"real-time":[74],"two-dimensional":[75],"planning":[77],"is":[78,102,117,146,158,206],"carried":[79],"out":[80],"based":[81],"obstacle":[84],"map":[85],"information":[86],"obtained":[87],"by":[88],"airborne":[89],"radar":[90],"in":[91,169],"real":[92],"time.":[93],"this":[95],"paper,":[96],"priority":[98],"experience":[99],"playback":[100],"mechanism":[101],"introduced":[103,118],"give":[105],"weight":[107],"important":[109],"samples":[110],"improve":[112],"efficiency.":[114],"Multi-step":[115],"accelerate":[120],"training":[122],"speed":[123],"enhance":[125],"stability":[127],"training.":[129],"Through":[130],"network":[132],"structure":[133],"Dueling":[135],"DQN,":[136],"value":[138],"evaluation":[139],"specific":[141],"states":[142],"their":[144],"actions":[145],"more":[147],"accurate.":[148],"The":[149],"experimental":[150],"results":[151],"show":[152],"improved":[155,203],"DQN":[156,167,204],"superior":[159],"A":[162],"*":[163],"terms":[170],"length":[173],"number":[176],"inflection":[178],"points,":[179],"ensures":[181],"economy":[183],"navigation":[187],"By":[190],"changing":[191],"position":[193],"movable":[196],"obstacle,":[197],"rerouting":[199,210,213],"ability":[200],"tested,":[207],"time":[214],"are":[215],"evaluated.":[216]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}