{"id":"https://openalex.org/W3002657127","doi":"https://doi.org/10.1109/lra.2020.2969145","title":"Bayesian Local Sampling-Based Planning","display_name":"Bayesian Local Sampling-Based Planning","publication_year":2020,"publication_date":"2020-01-24","ids":{"openalex":"https://openalex.org/W3002657127","doi":"https://doi.org/10.1109/lra.2020.2969145","mag":"3002657127"},"language":"en","primary_location":{"id":"doi:10.1109/lra.2020.2969145","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lra.2020.2969145","pdf_url":null,"source":{"id":"https://openalex.org/S4210169774","display_name":"IEEE Robotics and Automation Letters","issn_l":"2377-3766","issn":["2377-3766"],"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 Robotics and Automation Letters","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/A5016732447","display_name":"Tin Lai","orcid":"https://orcid.org/0000-0003-0641-5250"},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"The University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Tin Lai","raw_affiliation_strings":["School of Computer Science, The University of Sydney, Camperdown, Australia"],"raw_orcid":"https://orcid.org/0000-0003-0641-5250","affiliations":[{"raw_affiliation_string":"School of Computer Science, The University of Sydney, Camperdown, Australia","institution_ids":["https://openalex.org/I129604602"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038103937","display_name":"Philippe Morere","orcid":"https://orcid.org/0000-0001-6035-1724"},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"The University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Philippe Morere","raw_affiliation_strings":["School of Computer Science, The University of Sydney, Camperdown, Australia"],"raw_orcid":"https://orcid.org/0000-0001-6035-1724","affiliations":[{"raw_affiliation_string":"School of Computer Science, The University of Sydney, Camperdown, Australia","institution_ids":["https://openalex.org/I129604602"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062619542","display_name":"F\u00e1bio Ramos","orcid":"https://orcid.org/0000-0002-2996-2188"},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"The University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]},{"id":"https://openalex.org/I4210127875","display_name":"Nvidia (United States)","ror":"https://ror.org/03jdj4y14","country_code":"US","type":"company","lineage":["https://openalex.org/I4210127875"]}],"countries":["AU","US"],"is_corresponding":false,"raw_author_name":"Fabio Ramos","raw_affiliation_strings":["NVIDIA, Santa Clara, USA","School of Computer Science, The University of Sydney, Camperdown, Australia"],"raw_orcid":"https://orcid.org/0000-0002-2996-2188","affiliations":[{"raw_affiliation_string":"NVIDIA, Santa Clara, USA","institution_ids":["https://openalex.org/I4210127875"]},{"raw_affiliation_string":"School of Computer Science, The University of Sydney, Camperdown, Australia","institution_ids":["https://openalex.org/I129604602"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067160257","display_name":"Gilad Francis","orcid":"https://orcid.org/0000-0001-7910-8556"},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"The University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Gilad Francis","raw_affiliation_strings":["School of Computer Science, The University of Sydney, Camperdown, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, The University of Sydney, Camperdown, Australia","institution_ids":["https://openalex.org/I129604602"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.9367,"has_fulltext":false,"cited_by_count":43,"citation_normalized_percentile":{"value":0.92528545,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"5","issue":"2","first_page":"1954","last_page":"1961"},"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/T10653","display_name":"Robot Manipulation and Learning","score":0.9969000220298767,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9945999979972839,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.6487658619880676},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6001508235931396},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.5560158491134644},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5558456182479858},{"id":"https://openalex.org/keywords/adaptive-sampling","display_name":"Adaptive sampling","score":0.537983775138855},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5231791138648987},{"id":"https://openalex.org/keywords/completeness","display_name":"Completeness (order theory)","score":0.5123625993728638},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.5030059218406677},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.4940040707588196},{"id":"https://openalex.org/keywords/probabilistic-roadmap","display_name":"Probabilistic roadmap","score":0.4853343069553375},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4766008257865906},{"id":"https://openalex.org/keywords/probability-distribution","display_name":"Probability distribution","score":0.468117892742157},{"id":"https://openalex.org/keywords/motion-planning","display_name":"Motion planning","score":0.4589177072048187},{"id":"https://openalex.org/keywords/sampling-distribution","display_name":"Sampling distribution","score":0.4585222601890564},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.456922322511673},{"id":"https://openalex.org/keywords/sample-space","display_name":"Sample space","score":0.4491335451602936},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4183492660522461},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.26161879301071167},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.22466087341308594},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.13263341784477234},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.13194039463996887},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.10858699679374695}],"concepts":[{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.6487658619880676},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6001508235931396},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.5560158491134644},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5558456182479858},{"id":"https://openalex.org/C2781395549","wikidata":"https://www.wikidata.org/wiki/Q4680762","display_name":"Adaptive sampling","level":3,"score":0.537983775138855},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5231791138648987},{"id":"https://openalex.org/C17231256","wikidata":"https://www.wikidata.org/wiki/Q5156540","display_name":"Completeness (order theory)","level":2,"score":0.5123625993728638},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.5030059218406677},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.4940040707588196},{"id":"https://openalex.org/C2778803389","wikidata":"https://www.wikidata.org/wiki/Q7246866","display_name":"Probabilistic roadmap","level":4,"score":0.4853343069553375},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4766008257865906},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.468117892742157},{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.4589177072048187},{"id":"https://openalex.org/C167723999","wikidata":"https://www.wikidata.org/wiki/Q3773214","display_name":"Sampling distribution","level":2,"score":0.4585222601890564},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.456922322511673},{"id":"https://openalex.org/C100279318","wikidata":"https://www.wikidata.org/wiki/Q467440","display_name":"Sample space","level":2,"score":0.4491335451602936},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4183492660522461},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26161879301071167},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.22466087341308594},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.13263341784477234},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.13194039463996887},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.10858699679374695},{"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/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lra.2020.2969145","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lra.2020.2969145","pdf_url":null,"source":{"id":"https://openalex.org/S4210169774","display_name":"IEEE Robotics and Automation Letters","issn_l":"2377-3766","issn":["2377-3766"],"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 Robotics and Automation Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.7900000214576721}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W131069610","https://openalex.org/W1424654272","https://openalex.org/W1670942551","https://openalex.org/W1777783943","https://openalex.org/W1937587174","https://openalex.org/W1971086298","https://openalex.org/W1991290239","https://openalex.org/W2000643286","https://openalex.org/W2036075293","https://openalex.org/W2046356179","https://openalex.org/W2055201760","https://openalex.org/W2096636360","https://openalex.org/W2106473816","https://openalex.org/W2109212155","https://openalex.org/W2112765698","https://openalex.org/W2122724557","https://openalex.org/W2128990851","https://openalex.org/W2141664020","https://openalex.org/W2143826757","https://openalex.org/W2144836152","https://openalex.org/W2157543942","https://openalex.org/W2316237370","https://openalex.org/W2334018742","https://openalex.org/W2397499736","https://openalex.org/W2623293810","https://openalex.org/W2765140685","https://openalex.org/W2801348287","https://openalex.org/W2905148377","https://openalex.org/W2963439114","https://openalex.org/W2968427533","https://openalex.org/W6600569696","https://openalex.org/W6605295560","https://openalex.org/W6637067985","https://openalex.org/W6648231039","https://openalex.org/W6676022314","https://openalex.org/W6712734153"],"related_works":["https://openalex.org/W2313214390","https://openalex.org/W2107535513","https://openalex.org/W2108886409","https://openalex.org/W2014453071","https://openalex.org/W2890892537","https://openalex.org/W3011015278","https://openalex.org/W2892819105","https://openalex.org/W2352219667","https://openalex.org/W2995003016","https://openalex.org/W2044712064"],"abstract_inverted_index":{"Sampling-based":[0],"planning":[1,8],"is":[2,109,184],"the":[3,31,35,51,77,126,138,157],"predominant":[4],"paradigm":[5],"for":[6,99],"motion":[7,48,92],"in":[9,64,125,142],"robotics.":[10],"Most":[11],"sampling-based":[12,47,91],"planners":[13],"use":[14],"a":[15,89,95,146,160,178,182],"global":[16,36],"random":[17,58],"sampling":[18,103,141],"scheme":[19,98],"to":[20,60,76,120,149],"guarantee":[21],"probabilistic":[22],"completeness.":[23],"However,":[24,67],"most":[25],"schemes":[26],"are":[27],"often":[28],"inefficient":[29],"as":[30],"samples":[32,61],"drawn":[33],"from":[34,132],"proposal":[37,104,107,180],"distribution,":[38,162,181],"and":[39,79,123,190],"do":[40,70],"not":[41,71],"exploit":[42],"relevant":[43],"local":[44,90,121],"structures.":[45],"Local":[46],"planners,":[49],"on":[50,113],"other":[52],"hand,":[53],"take":[54],"sequential":[55],"decisions":[56],"of":[57,81,140,159,167],"walks":[59],"valid":[62],"trajectories":[63,151],"configuration":[65,127],"space.":[66,128],"current":[68],"approaches":[69],"adapt":[72],"their":[73],"strategies":[74],"according":[75,119],"success":[78],"failures":[80],"past":[82,133],"samples.":[83],"In":[84],"this":[85,170],"work,":[86],"we":[87,136],"introduce":[88],"planner":[93],"with":[94,164],"Bayesian":[96,179],"learning":[97,131],"modelling":[100],"an":[101],"adaptive":[102],"distribution.":[105,171],"The":[106],"distribution":[108],"sequentially":[110,168],"updated":[111],"based":[112],"previous":[114],"samples,":[115,189],"consequently":[116],"shaping":[117],"it":[118],"obstacles":[122],"constraints":[124],"Thus,":[129],"through":[130],"observed":[134],"outcomes,":[135],"maximise":[137],"likelihood":[139],"regions":[143],"that":[144,175],"have":[145],"higher":[147],"probability":[148],"form":[150],"within":[152],"narrow":[153],"passages.":[154],"We":[155,172],"provide":[156],"formulation":[158],"sample-efficient":[161],"along":[163],"theoretical":[165],"foundation":[166],"updating":[169],"demonstrate":[173],"experimentally":[174],"by":[176],"using":[177],"solution":[183],"found":[185],"faster,":[186],"requiring":[187],"fewer":[188],"without":[191],"any":[192],"noticeable":[193],"performance":[194],"overhead.":[195]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":12},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
