{"id":"https://openalex.org/W2138979254","doi":"https://doi.org/10.1109/iros.2009.5354185","title":"Adaptive node sampling method for probabilistic roadmap planners","display_name":"Adaptive node sampling method for probabilistic roadmap planners","publication_year":2009,"publication_date":"2009-10-01","ids":{"openalex":"https://openalex.org/W2138979254","doi":"https://doi.org/10.1109/iros.2009.5354185","mag":"2138979254"},"language":"en","primary_location":{"id":"doi:10.1109/iros.2009.5354185","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros.2009.5354185","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE/RSJ International Conference on Intelligent Robots and Systems","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/A5053812196","display_name":"Byungjae Park","orcid":null},"institutions":[{"id":"https://openalex.org/I123900574","display_name":"Pohang University of Science and Technology","ror":"https://ror.org/04xysgw12","country_code":"KR","type":"education","lineage":["https://openalex.org/I123900574"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Byungjae Park","raw_affiliation_strings":["Robotics Laboratory Mechanical Engineering, Pohang University of Science and Technology, Pohang, South Korea","Robotics Lab., Mechanical Engineering, Pohang University of Science and Technology (POSTECH), KOREA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Robotics Laboratory Mechanical Engineering, Pohang University of Science and Technology, Pohang, South Korea","institution_ids":["https://openalex.org/I123900574"]},{"raw_affiliation_string":"Robotics Lab., Mechanical Engineering, Pohang University of Science and Technology (POSTECH), KOREA","institution_ids":["https://openalex.org/I123900574"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020648634","display_name":"Wan Kyun Chung","orcid":"https://orcid.org/0000-0003-2715-5320"},"institutions":[{"id":"https://openalex.org/I123900574","display_name":"Pohang University of Science and Technology","ror":"https://ror.org/04xysgw12","country_code":"KR","type":"education","lineage":["https://openalex.org/I123900574"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Wan Kyun Chung","raw_affiliation_strings":["Faculty of Mechanical Engineering, Pohang University of Science and Technology, Pohang, South Korea","Faculty of Mechanical Engineering, Pohang University of Science and Technology (POSTECH), KOREA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Mechanical Engineering, Pohang University of Science and Technology, Pohang, South Korea","institution_ids":["https://openalex.org/I123900574"]},{"raw_affiliation_string":"Faculty of Mechanical Engineering, Pohang University of Science and Technology (POSTECH), KOREA","institution_ids":["https://openalex.org/I123900574"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I123900574"],"apc_list":null,"apc_paid":null,"fwci":1.3139,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.83339307,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"4399","last_page":"4405"},"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":0.9958000183105469,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.994700014591217,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/probabilistic-roadmap","display_name":"Probabilistic roadmap","score":0.8395400643348694},{"id":"https://openalex.org/keywords/voronoi-diagram","display_name":"Voronoi diagram","score":0.742302417755127},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.6930003762245178},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6436132788658142},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.6173492670059204},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5670042634010315},{"id":"https://openalex.org/keywords/planner","display_name":"Planner","score":0.5619905591011047},{"id":"https://openalex.org/keywords/centroidal-voronoi-tessellation","display_name":"Centroidal Voronoi tessellation","score":0.4605099558830261},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.45955976843833923},{"id":"https://openalex.org/keywords/adaptive-sampling","display_name":"Adaptive sampling","score":0.4462505578994751},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.41186100244522095},{"id":"https://openalex.org/keywords/motion-planning","display_name":"Motion planning","score":0.3613387942314148},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.33357149362564087},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.2819969058036804},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2418590486049652},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.22074148058891296},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.12763634324073792},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12550368905067444},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.1047753393650055},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.09696060419082642},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.08448484539985657}],"concepts":[{"id":"https://openalex.org/C2778803389","wikidata":"https://www.wikidata.org/wiki/Q7246866","display_name":"Probabilistic roadmap","level":4,"score":0.8395400643348694},{"id":"https://openalex.org/C24881265","wikidata":"https://www.wikidata.org/wiki/Q757267","display_name":"Voronoi diagram","level":2,"score":0.742302417755127},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.6930003762245178},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6436132788658142},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6173492670059204},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5670042634010315},{"id":"https://openalex.org/C2776999362","wikidata":"https://www.wikidata.org/wiki/Q2349274","display_name":"Planner","level":2,"score":0.5619905591011047},{"id":"https://openalex.org/C205672865","wikidata":"https://www.wikidata.org/wiki/Q5062961","display_name":"Centroidal Voronoi tessellation","level":3,"score":0.4605099558830261},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.45955976843833923},{"id":"https://openalex.org/C2781395549","wikidata":"https://www.wikidata.org/wiki/Q4680762","display_name":"Adaptive sampling","level":3,"score":0.4462505578994751},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.41186100244522095},{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.3613387942314148},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33357149362564087},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.2819969058036804},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2418590486049652},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.22074148058891296},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.12763634324073792},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12550368905067444},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.1047753393650055},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.09696060419082642},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.08448484539985657},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iros.2009.5354185","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros.2009.5354185","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE/RSJ International Conference on Intelligent Robots and Systems","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8100000023841858,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W198732646","https://openalex.org/W1521785144","https://openalex.org/W1549060199","https://openalex.org/W1937587174","https://openalex.org/W2016828152","https://openalex.org/W2051752778","https://openalex.org/W2101043416","https://openalex.org/W2107535513","https://openalex.org/W2111308925","https://openalex.org/W2127218421","https://openalex.org/W2128990851","https://openalex.org/W2141161225","https://openalex.org/W2150593711","https://openalex.org/W2151402824","https://openalex.org/W2162203349","https://openalex.org/W2752885492","https://openalex.org/W3004540582","https://openalex.org/W6608150121"],"related_works":["https://openalex.org/W2059217232","https://openalex.org/W1992160534","https://openalex.org/W2371724110","https://openalex.org/W1930867958","https://openalex.org/W2053576657","https://openalex.org/W1995318580","https://openalex.org/W4206423751","https://openalex.org/W2041214247","https://openalex.org/W2133344964","https://openalex.org/W2051752778"],"abstract_inverted_index":{"This":[0,36],"paper":[1],"proposes":[2],"an":[3],"adaptive":[4],"node":[5],"sampling":[6,20],"method":[7,16,37,53,87,111],"for":[8],"the":[9,18,22,26,31,34,48,51,58,63,68,80,89,95,99,104,109],"probabilistic":[10],"roadmap":[11],"(PRM)":[12],"planner.":[13],"The":[14,85],"proposed":[15,52,86,110],"substitutes":[17],"random":[19],"in":[21,44,117],"learning":[23],"phase":[24,40],"of":[25,33,70,79,94],"PRM":[27,105],"planner":[28,106],"and":[29,62,92,103],"improves":[30],"configuration":[32],"roadmap.":[35,49],"uses":[38],"two":[39],"to":[41,46,97],"determine":[42],"nodes":[43,56,72,96],"order":[45],"construct":[47],"First,":[50],"extracts":[54],"initial":[55],"using":[57,75],"approximated":[59],"cell":[60],"decomposition":[61],"Harris":[64],"corner":[65],"detector.":[66],"Second,":[67],"positions":[69,93],"these":[71],"are":[73],"optimized":[74],"a":[76],"construction":[77],"process":[78],"centroidal":[81],"voronoi":[82],"tessellation":[83],"(CVT).":[84],"determines":[88],"adequate":[90],"number":[91],"represent":[98],"entire":[100],"free":[101],"space,":[102],"based":[107],"on":[108],"finds":[112],"out":[113],"efficient":[114],"paths":[115],"even":[116],"narrow":[118],"passages.":[119],"These":[120],"properties":[121],"have":[122],"been":[123],"verified":[124],"though":[125],"experiments.":[126]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":1},{"year":2012,"cited_by_count":3}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
