{"id":"https://openalex.org/W7124941115","doi":"https://doi.org/10.1109/tiv.2026.3656305","title":"Decentralized Multi-Agent Motion Planning Using Cognitive Hierarchy and Gaussian Process Classification","display_name":"Decentralized Multi-Agent Motion Planning Using Cognitive Hierarchy and Gaussian Process Classification","publication_year":2026,"publication_date":"2026-01-20","ids":{"openalex":"https://openalex.org/W7124941115","doi":"https://doi.org/10.1109/tiv.2026.3656305"},"language":null,"primary_location":{"id":"doi:10.1109/tiv.2026.3656305","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tiv.2026.3656305","pdf_url":null,"source":{"id":"https://openalex.org/S4210199657","display_name":"IEEE Transactions on Intelligent Vehicles","issn_l":"2379-8858","issn":["2379-8858","2379-8904"],"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 Intelligent Vehicles","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":"https://openalex.org/A5005904658","display_name":"Josh Netter","orcid":"https://orcid.org/0009-0001-0566-9024"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Josh Netter","raw_affiliation_strings":["Daniel Guggenheim School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, USA"],"raw_orcid":"https://orcid.org/0009-0001-0566-9024","affiliations":[{"raw_affiliation_string":"Daniel Guggenheim School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, USA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039559939","display_name":"George P. Kontoudis","orcid":"https://orcid.org/0000-0003-2193-7700"},"institutions":[{"id":"https://openalex.org/I167576493","display_name":"Colorado School of Mines","ror":"https://ror.org/04raf6v53","country_code":"US","type":"education","lineage":["https://openalex.org/I167576493"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"George P. Kontoudis","raw_affiliation_strings":["Department of Mechanical Engineering, Colorado School of Mines, Golden, CO, USA"],"raw_orcid":"https://orcid.org/0000-0003-2193-7700","affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering, Colorado School of Mines, Golden, CO, USA","institution_ids":["https://openalex.org/I167576493"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040301558","display_name":"Kyriakos G. Vamvoudakis","orcid":"https://orcid.org/0000-0003-1978-4848"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kyriakos G. Vamvoudakis","raw_affiliation_strings":["Daniel Guggenheim School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, USA"],"raw_orcid":"https://orcid.org/0000-0003-1978-4848","affiliations":[{"raw_affiliation_string":"Daniel Guggenheim School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, USA","institution_ids":["https://openalex.org/I130701444"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.3672,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.91348981,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"11","issue":"3","first_page":"391","last_page":"400"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10586","display_name":"Robotic Path Planning Algorithms","score":0.3172000050544739,"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":0.3172000050544739,"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/T11500","display_name":"Evacuation and Crowd Dynamics","score":0.25360000133514404,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.031700000166893005,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/motion-planning","display_name":"Motion planning","score":0.7289999723434448},{"id":"https://openalex.org/keywords/hierarchy","display_name":"Hierarchy","score":0.5511000156402588},{"id":"https://openalex.org/keywords/obstacle","display_name":"Obstacle","score":0.5309000015258789},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.5288000106811523},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.5238000154495239},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.5138000249862671},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4925999939441681}],"concepts":[{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.7289999723434448},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5845999717712402},{"id":"https://openalex.org/C31170391","wikidata":"https://www.wikidata.org/wiki/Q188619","display_name":"Hierarchy","level":2,"score":0.5511000156402588},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.545199990272522},{"id":"https://openalex.org/C2776650193","wikidata":"https://www.wikidata.org/wiki/Q264661","display_name":"Obstacle","level":2,"score":0.5309000015258789},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.5288000106811523},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.5238000154495239},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.5138000249862671},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4925999939441681},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.45339998602867126},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.453000009059906},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42100000381469727},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.36399999260902405},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.3610000014305115},{"id":"https://openalex.org/C6683253","wikidata":"https://www.wikidata.org/wiki/Q7075535","display_name":"Obstacle avoidance","level":4,"score":0.3395000100135803},{"id":"https://openalex.org/C81692654","wikidata":"https://www.wikidata.org/wiki/Q225926","display_name":"Kriging","level":2,"score":0.33809998631477356},{"id":"https://openalex.org/C170494330","wikidata":"https://www.wikidata.org/wiki/Q1778434","display_name":"Cognitive map","level":3,"score":0.31869998574256897},{"id":"https://openalex.org/C201717286","wikidata":"https://www.wikidata.org/wiki/Q938185","display_name":"Rationality","level":2,"score":0.25940001010894775}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tiv.2026.3656305","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tiv.2026.3656305","pdf_url":null,"source":{"id":"https://openalex.org/S4210199657","display_name":"IEEE Transactions on Intelligent Vehicles","issn_l":"2379-8858","issn":["2379-8858","2379-8904"],"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 Intelligent Vehicles","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.8226044774055481,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1510672157","https://openalex.org/W2048820947","https://openalex.org/W2082585576","https://openalex.org/W2114765979","https://openalex.org/W2140542425","https://openalex.org/W2335799662","https://openalex.org/W2564717627","https://openalex.org/W2787690100","https://openalex.org/W2809731556","https://openalex.org/W2810655707","https://openalex.org/W2811438975","https://openalex.org/W2907126380","https://openalex.org/W2917721237","https://openalex.org/W2927564896","https://openalex.org/W2971620216","https://openalex.org/W2994015487","https://openalex.org/W3008967682","https://openalex.org/W3034603778","https://openalex.org/W3045857947","https://openalex.org/W3134972302","https://openalex.org/W3168913804","https://openalex.org/W3174967682","https://openalex.org/W4210417851","https://openalex.org/W4293064666","https://openalex.org/W4382935903","https://openalex.org/W4386231185","https://openalex.org/W4402572548","https://openalex.org/W7114925706"],"related_works":[],"abstract_inverted_index":{"In":[0],"this":[1,173],"paper,":[2],"we":[3],"present":[4],"a":[5,27,35,49,91,123,145,160],"motion":[6,42,114,132],"planning":[7,115],"algorithm":[8],"designed":[9],"to":[10,39,133,183],"guide":[11],"agents,":[12,16],"termed":[13],"as":[14,58],"player":[15,47,120,138],"optimally":[17],"through":[18,51],"multi-agent":[19],"3D":[20,161],"urban":[21,162],"air":[22,163],"environments.":[23],"The":[24,77],"method":[25,95,155],"integrates":[26],"sampling-based":[28],"path":[29,50,143],"planner,":[30],"model-free":[31],"optimal":[32],"control,":[33],"and":[34,71,167,192],"cognitive":[36,78],"hierarchy":[37,79],"model":[38,80],"predict":[40],"the":[41,52,59,63,72,82,88,119,150,153,188],"of":[43,62,74,84,101,152,159,190],"other":[44],"agents.":[45,169],"Each":[46,137],"constructs":[48],"environment,":[53,89],"which":[54],"is":[55,117,175],"dynamically":[56],"re-planned":[57],"obstacle":[60,125],"space":[61,126],"environment":[64,164],"evolves":[65],"based":[66,127],"on":[67,128],"its":[68,141],"online":[69],"observations":[70,73],"cooperating":[75],"players.":[76],"predicts":[81],"behavior":[83],"each":[85,107,129],"agent":[86],"in":[87,103,156],"while":[90],"Gaussian":[92],"process":[93],"classification":[94],"estimates":[96],"an":[97],"unknown":[98],"agent's":[99,108,113,130],"level":[100],"rationality":[102],"real-time":[104],"by":[105,181],"observing":[106],"kinodynamic":[109],"distance.":[110],"Once":[111],"another":[112],"strategy":[116],"inferred,":[118],"agents":[121,182],"construct":[122],"predicted":[124],"expected":[131],"avoid":[134],"potential":[135],"collisions.":[136],"then":[139],"traverses":[140],"planned":[142],"using":[144],"Q-learning":[146],"controller.":[147],"We":[148,170],"validate":[149],"effectiveness":[151],"proposed":[154],"numerical":[157],"experiments":[158],"containing":[165],"four":[166],"ten":[168],"demonstrate":[171],"that":[172],"approach":[174],"effective":[176],"for":[177],"reducing":[178],"distance":[179],"traveled":[180],"reach":[184],"their":[185],"goals,":[186],"mitigating":[187],"risk":[189],"collisions,":[191],"preventing":[193],"deadlocks.":[194]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-08-12T21:12:35.861297","created_date":"2026-01-21T00:00:00"}
