{"id":"https://openalex.org/W4391493715","doi":"https://doi.org/10.1145/3603273.3635053","title":"A Path Planning Framework for Air-land Amphibious UAV with Fast Response and Fine Learning","display_name":"A Path Planning Framework for Air-land Amphibious UAV with Fast Response and Fine Learning","publication_year":2023,"publication_date":"2023-11-18","ids":{"openalex":"https://openalex.org/W4391493715","doi":"https://doi.org/10.1145/3603273.3635053"},"language":"en","primary_location":{"id":"doi:10.1145/3603273.3635053","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3603273.3635053","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 International Conference on Advances in Artificial Intelligence and Applications","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/A5078698144","display_name":"Xinghong Yang","orcid":"https://orcid.org/0009-0004-9481-5223"},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xinghong Yang","raw_affiliation_strings":["University of Science and Technology Beijing, China"],"raw_orcid":"https://orcid.org/0009-0004-9481-5223","affiliations":[{"raw_affiliation_string":"University of Science and Technology Beijing, China","institution_ids":["https://openalex.org/I92403157"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103169060","display_name":"Heqing Li","orcid":"https://orcid.org/0000-0002-4011-4526"},"institutions":[{"id":"https://openalex.org/I119454577","display_name":"Nanjing Agricultural University","ror":"https://ror.org/05td3s095","country_code":"CN","type":"education","lineage":["https://openalex.org/I119454577"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Heqing Li","raw_affiliation_strings":["Nanjing Agricultural University, China"],"raw_orcid":"https://orcid.org/0000-0002-4011-4526","affiliations":[{"raw_affiliation_string":"Nanjing Agricultural University, China","institution_ids":["https://openalex.org/I119454577"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5078698144"],"corresponding_institution_ids":["https://openalex.org/I92403157"],"apc_list":null,"apc_paid":null,"fwci":0.1177,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.45871943,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"76","last_page":"79"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10586","display_name":"Robotic Path Planning Algorithms","score":1.0,"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":1.0,"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":0.9988999962806702,"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/T10879","display_name":"Robotic Locomotion and Control","score":0.9941999912261963,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/motion-planning","display_name":"Motion planning","score":0.809525191783905},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.7932752370834351},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7342298626899719},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6131494641304016},{"id":"https://openalex.org/keywords/terrain","display_name":"Terrain","score":0.5959181785583496},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.5945542454719543},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5421037077903748},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.48777180910110474},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.4674566388130188},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38837042450904846},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.23794129490852356},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.22537484765052795},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.08389124274253845}],"concepts":[{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.809525191783905},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7932752370834351},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7342298626899719},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6131494641304016},{"id":"https://openalex.org/C161840515","wikidata":"https://www.wikidata.org/wiki/Q186131","display_name":"Terrain","level":2,"score":0.5959181785583496},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.5945542454719543},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5421037077903748},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.48777180910110474},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.4674566388130188},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38837042450904846},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.23794129490852356},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.22537484765052795},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.08389124274253845},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3603273.3635053","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3603273.3635053","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 International Conference on Advances in Artificial Intelligence and Applications","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.5400000214576721,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W2560787352","https://openalex.org/W2970414985","https://openalex.org/W3006052720","https://openalex.org/W3094077559","https://openalex.org/W3108449827","https://openalex.org/W3208321106","https://openalex.org/W3215417452","https://openalex.org/W3217370814","https://openalex.org/W4220818477","https://openalex.org/W4223954537","https://openalex.org/W4229034698","https://openalex.org/W4231970068","https://openalex.org/W4281796415","https://openalex.org/W4304698290","https://openalex.org/W4320801552","https://openalex.org/W4360619340","https://openalex.org/W6810250913","https://openalex.org/W6811613075","https://openalex.org/W6851452591"],"related_works":["https://openalex.org/W4285089922","https://openalex.org/W2183638083","https://openalex.org/W2359600231","https://openalex.org/W2119138398","https://openalex.org/W2380019117","https://openalex.org/W2598979711","https://openalex.org/W2037384153","https://openalex.org/W3138952546","https://openalex.org/W2306351532","https://openalex.org/W1987886368"],"abstract_inverted_index":{"Air-land":[0],"amphibious":[1],"unmanned":[2],"aerial":[3,11],"vehicle":[4],"(ALAUAV),":[5],"which":[6],"integrate":[7],"ground":[8],"mobility":[9],"and":[10,20,42,57,76,94,105,111,141],"flight":[12],"capabilities,":[13],"are":[14],"versatile":[15],"intelligent":[16],"entities.":[17],"Efficient":[18],"global":[19],"precise":[21],"local":[22,123],"path":[23,55,66,125,154],"planning":[24,67,155],"is":[25],"of":[26,34,73,131,152,160,171],"paramount":[27],"significance":[28],"in":[29,53,138,157,174],"ensuring":[30],"the":[31,71,129,149,158,169],"high-quality":[32],"execution":[33],"their":[35],"missions,":[36],"as":[37,39],"well":[38],"system":[40],"safety":[41],"stability.":[43],"However,":[44],"current":[45],"methodologies":[46],"often":[47],"focus":[48],"on":[49,70],"single":[50],"aspects,":[51],"resulting":[52],"complex":[54,175],"computations":[56],"inadequate":[58],"precision.":[59],"In":[60,79],"this":[61,172],"study,":[62],"we":[63,83,114,134],"propose":[64],"a":[65,116,163],"framework":[68],"based":[69],"integration":[72],"multiple":[74],"perspectives":[75],"reinforcement":[77,118],"learning.":[78],"addressing":[80],"known":[81,140],"environments,":[82,113],"transform":[84],"intricate":[85],"three-dimensional":[86],"(3D)":[87],"terrains":[88],"into":[89],"two-dimensional":[90],"(2D)":[91],"multi-view":[92],"representations":[93],"perform":[95],"parallel":[96],"computations,":[97],"effectively":[98],"resolving":[99],"issues":[100],"related":[101],"to":[102,121],"delayed":[103],"responsiveness":[104],"reduced":[106],"efficiency.":[107],"Subsequently,":[108],"for":[109,166],"dynamic":[110],"unknown":[112,142],"establish":[115],"deep":[117],"learning":[119],"model":[120],"achieve":[122],"fine-grained":[124],"planning.":[126],"To":[127],"demonstrate":[128,148],"effectiveness":[130],"our":[132,153],"approach,":[133],"conduct":[135],"simulation":[136],"experiments":[137],"both":[139],"environments.":[143],"The":[144],"experimental":[145],"results":[146],"unequivocally":[147],"superior":[150],"performance":[151],"method":[156],"context":[159],"ALAUAV,":[161],"laying":[162],"robust":[164],"foundation":[165],"further":[167],"expanding":[168],"application":[170],"technology":[173],"scenarios.":[176]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
