{"id":"https://openalex.org/W7083023005","doi":"https://doi.org/10.1109/tro.2025.3613568","title":"Learning-Based Motion Planning Leveraging Multivariate Deep Evidential Regression","display_name":"Learning-Based Motion Planning Leveraging Multivariate Deep Evidential Regression","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W7083023005","doi":"https://doi.org/10.1109/tro.2025.3613568"},"language":"en","primary_location":{"id":"doi:10.1109/tro.2025.3613568","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tro.2025.3613568","pdf_url":null,"source":{"id":"https://openalex.org/S144620930","display_name":"IEEE Transactions on Robotics","issn_l":"1552-3098","issn":["1552-3098","1546-1904","1941-0468"],"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 Robotics","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":null,"display_name":"Rixin Wang","orcid":"https://orcid.org/0009-0004-6716-8534"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rixin Wang","raw_affiliation_strings":["School of Mechanical Engineering and Automation, Northeastern University, Shenyang, China"],"raw_orcid":"https://orcid.org/0009-0004-6716-8534","affiliations":[{"raw_affiliation_string":"School of Mechanical Engineering and Automation, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Shuopeng Wang","orcid":"https://orcid.org/0000-0003-0780-7037"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuopeng Wang","raw_affiliation_strings":["School of Mechanical Engineering and Automation, Northeastern University, Shenyang, China"],"raw_orcid":"https://orcid.org/0000-0003-0780-7037","affiliations":[{"raw_affiliation_string":"School of Mechanical Engineering and Automation, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jintao Ye","orcid":"https://orcid.org/0000-0002-0280-5836"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jintao Ye","raw_affiliation_strings":["School of Mechanical Engineering and Automation, Northeastern University, Shenyang, China"],"raw_orcid":"https://orcid.org/0000-0002-0280-5836","affiliations":[{"raw_affiliation_string":"School of Mechanical Engineering and Automation, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Ying Zhang","orcid":"https://orcid.org/0000-0002-7560-9764"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ying Zhang","raw_affiliation_strings":["School of Mechanical Engineering and Automation, Northeastern University, Shenyang, China"],"raw_orcid":"https://orcid.org/0000-0002-7560-9764","affiliations":[{"raw_affiliation_string":"School of Mechanical Engineering and Automation, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"last","author":{"id":null,"display_name":"Lina Hao","orcid":"https://orcid.org/0000-0001-8791-2253"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lina Hao","raw_affiliation_strings":["School of Mechanical Engineering and Automation, Northeastern University, Shenyang, China"],"raw_orcid":"https://orcid.org/0000-0001-8791-2253","affiliations":[{"raw_affiliation_string":"School of Mechanical Engineering and Automation, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I9224756"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.64639894,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"41","issue":null,"first_page":"5816","last_page":"5834"},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.6715999841690063,"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"}},"topics":[{"id":"https://openalex.org/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.6715999841690063,"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/T13067","display_name":"Geological Modeling and Analysis","score":0.031099999323487282,"subfield":{"id":"https://openalex.org/subfields/1906","display_name":"Geochemistry and Petrology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T14311","display_name":"Electrical and Electromagnetic Research","score":0.01889999955892563,"subfield":{"id":"https://openalex.org/subfields/3107","display_name":"Atomic and Molecular Physics, and Optics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.7440000176429749},{"id":"https://openalex.org/keywords/motion-planning","display_name":"Motion planning","score":0.6080999970436096},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.5218999981880188},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5210999846458435},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4702000021934509},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4496999979019165},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40149998664855957},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.3986000120639801}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8036999702453613},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7440000176429749},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6337000131607056},{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.6080999970436096},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.5218999981880188},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5210999846458435},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4878999888896942},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4702000021934509},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4496999979019165},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40149998664855957},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3986000120639801},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3700999915599823},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.3668000102043152},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.36010000109672546},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.31700000166893005},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.3138999938964844},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.29820001125335693},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.2937000095844269},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.2833000123500824},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.28290000557899475},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.2806999981403351},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.2793999910354614},{"id":"https://openalex.org/C2776999362","wikidata":"https://www.wikidata.org/wiki/Q2349274","display_name":"Planner","level":2,"score":0.2775000035762787}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tro.2025.3613568","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tro.2025.3613568","pdf_url":null,"source":{"id":"https://openalex.org/S144620930","display_name":"IEEE Transactions on Robotics","issn_l":"1552-3098","issn":["1552-3098","1546-1904","1941-0468"],"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 Robotics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Climate action","id":"https://metadata.un.org/sdg/13","score":0.4713403284549713}],"awards":[{"id":"https://openalex.org/G1806596581","display_name":null,"funder_award_id":"62573107","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2379173535","display_name":null,"funder_award_id":"N2403020","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G4074719757","display_name":null,"funder_award_id":"62461160260","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1976930960","https://openalex.org/W2110762409","https://openalex.org/W2141664020","https://openalex.org/W2894417726","https://openalex.org/W2905288042","https://openalex.org/W2909147555","https://openalex.org/W2914157382","https://openalex.org/W2967969632","https://openalex.org/W3003648623","https://openalex.org/W3011646207","https://openalex.org/W3033739625","https://openalex.org/W3047385205","https://openalex.org/W3096541186","https://openalex.org/W3173376954","https://openalex.org/W3183048323","https://openalex.org/W4225899372","https://openalex.org/W4226218507","https://openalex.org/W4286609354","https://openalex.org/W4309471863","https://openalex.org/W4366371396","https://openalex.org/W4382317663","https://openalex.org/W4387350600","https://openalex.org/W4392908006","https://openalex.org/W4393157446","https://openalex.org/W4393161139","https://openalex.org/W4396604981","https://openalex.org/W4401414165","https://openalex.org/W4401416446"],"related_works":[],"abstract_inverted_index":{"Learning-based":[0],"motion":[1,44],"planning":[2,45,116],"methods":[3],"have":[4],"shown":[5],"significant":[6],"promise":[7],"in":[8,21],"enhancing":[9],"the":[10,30,83,120,129,139],"efficiency":[11],"of":[12,34,78,123],"traditional":[13],"algorithms.":[14],"However,":[15],"they":[16],"often":[17],"face":[18],"performance":[19],"degradation":[20],"novel":[22],"environments":[23],"with":[24],"drastic":[25],"scene":[26],"changes":[27],"due":[28],"to":[29,118,131,137],"limited":[31],"generalization":[32,121],"ability":[33,122],"deep":[35],"neural":[36,73,98],"networks":[37],"(DNNs).":[38],"This":[39],"paper":[40],"introduces":[41],"a":[42,49,54,70,96,101,132],"confidence-driven":[43,55],"network":[46,57],"(CDMPNet),":[47],"comprising":[48],"feature":[50],"extraction":[51],"autoencoder":[52,60],"and":[53,87,100,106,114],"sampling":[56],"(CDSNet).":[58],"The":[59,67],"compresses":[61],"point":[62],"clouds":[63],"into":[64],"latent":[65],"vectors.":[66],"CDSNet":[68],"is":[69],"closed-form":[71],"continuous-time":[72],"network,":[74],"which":[75],"predicts":[76],"hyperparameters":[77],"an":[79],"evidential":[80],"distribution":[81],"over":[82],"subsequent":[84],"state's":[85],"mean":[86],"covariance":[88],"for":[89,141],"robot":[90],"configuration":[91],"sampling.":[92],"We":[93],"also":[94],"present":[95],"CDMPNet-based":[97],"planner":[99],"CDMPNet-guided":[102],"RRTConnect":[103],"algorithm.":[104],"Simulations":[105],"ablation":[107],"studies":[108],"are":[109],"conducted":[110],"on":[111],"2-D,":[112],"3-D,":[113],"7-D":[115],"tasks":[117],"validate":[119],"our":[124],"method.":[125],"Furthermore,":[126],"we":[127],"transfer":[128],"approach":[130],"7-DOF":[133],"Sawyer":[134],"robotic":[135],"arm":[136],"demonstrate":[138],"potential":[140],"real-world":[142],"deployment.":[143]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
