{"id":"https://openalex.org/W4416750761","doi":"https://doi.org/10.1109/iros60139.2025.11246858","title":"CIT*: Context-Based Biased Batch-Sampling for Almost-Surely Asymptotically Optimal Motion Planning","display_name":"CIT*: Context-Based Biased Batch-Sampling for Almost-Surely Asymptotically Optimal Motion Planning","publication_year":2025,"publication_date":"2025-10-19","ids":{"openalex":"https://openalex.org/W4416750761","doi":"https://doi.org/10.1109/iros60139.2025.11246858"},"language":null,"primary_location":{"id":"doi:10.1109/iros60139.2025.11246858","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros60139.2025.11246858","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","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/A5111122200","display_name":"Liding Zhang","orcid":"https://orcid.org/0009-0007-0119-706X"},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Liding Zhang","raw_affiliation_strings":["Technical University of Munich,Department of Informatics,Germany"],"affiliations":[{"raw_affiliation_string":"Technical University of Munich,Department of Informatics,Germany","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101467043","display_name":"Yali Wei","orcid":"https://orcid.org/0000-0002-7491-1558"},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Yankun Wei","raw_affiliation_strings":["Technical University of Munich,Department of Informatics,Germany"],"affiliations":[{"raw_affiliation_string":"Technical University of Munich,Department of Informatics,Germany","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054771935","display_name":"Kuanqi Cai","orcid":"https://orcid.org/0000-0002-3655-8116"},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Kuanqi Cai","raw_affiliation_strings":["Technical University of Munich,Department of Informatics,Germany"],"affiliations":[{"raw_affiliation_string":"Technical University of Munich,Department of Informatics,Germany","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060444894","display_name":"Zhenshan Bing","orcid":"https://orcid.org/0000-0002-0896-2517"},"institutions":[{"id":"https://openalex.org/I308837","display_name":"Suzhou University of Science and Technology","ror":"https://ror.org/04en8wb91","country_code":"CN","type":"education","lineage":["https://openalex.org/I308837"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenshan Bing","raw_affiliation_strings":["Nanjing University (Suzhou Campus),State Key Laboratory for Novel Software Technology and the School of Science and Technology,China"],"affiliations":[{"raw_affiliation_string":"Nanjing University (Suzhou Campus),State Key Laboratory for Novel Software Technology and the School of Science and Technology,China","institution_ids":["https://openalex.org/I308837"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030898354","display_name":"Meng Yuan","orcid":"https://orcid.org/0000-0003-4176-0164"},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Yuan Meng","raw_affiliation_strings":["Technical University of Munich,Department of Informatics,Germany"],"affiliations":[{"raw_affiliation_string":"Technical University of Munich,Department of Informatics,Germany","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101299293","display_name":"Fan Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Fan Wu","raw_affiliation_strings":["Technical University of Munich,Department of Informatics,Germany"],"affiliations":[{"raw_affiliation_string":"Technical University of Munich,Department of Informatics,Germany","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024171209","display_name":"Sami Haddadin","orcid":"https://orcid.org/0000-0001-7696-4955"},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Sami Haddadin","raw_affiliation_strings":["Technical University of Munich,Department of Informatics,Germany"],"affiliations":[{"raw_affiliation_string":"Technical University of Munich,Department of Informatics,Germany","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063781430","display_name":"Alois Knoll","orcid":"https://orcid.org/0000-0003-4840-076X"},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Alois Knoll","raw_affiliation_strings":["Technical University of Munich,Department of Informatics,Germany"],"affiliations":[{"raw_affiliation_string":"Technical University of Munich,Department of Informatics,Germany","institution_ids":["https://openalex.org/I62916508"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5111122200"],"corresponding_institution_ids":["https://openalex.org/I62916508"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.40323966,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4421","last_page":"4428"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10586","display_name":"Robotic Path Planning Algorithms","score":0.7965999841690063,"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":0.7965999841690063,"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":0.05000000074505806,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.0414000004529953,"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/weighting","display_name":"Weighting","score":0.5788999795913696},{"id":"https://openalex.org/keywords/motion-planning","display_name":"Motion planning","score":0.5146999955177307},{"id":"https://openalex.org/keywords/kernel-density-estimation","display_name":"Kernel density estimation","score":0.5101000070571899},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5073999762535095},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.49320000410079956},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.49070000648498535},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.47279998660087585},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.43880000710487366},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4307999908924103}],"concepts":[{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.5788999795913696},{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.5146999955177307},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.5101000070571899},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5073999762535095},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5062000155448914},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.49320000410079956},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.49070000648498535},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.47279998660087585},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4528999924659729},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.43880000710487366},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4307999908924103},{"id":"https://openalex.org/C75917345","wikidata":"https://www.wikidata.org/wiki/Q2725298","display_name":"Sampling bias","level":3,"score":0.42480000853538513},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.4221000075340271},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.42100000381469727},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.362199991941452},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.36169999837875366},{"id":"https://openalex.org/C171268870","wikidata":"https://www.wikidata.org/wiki/Q1486676","display_name":"GRASP","level":2,"score":0.35359999537467957},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.352400004863739},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3513000011444092},{"id":"https://openalex.org/C81692654","wikidata":"https://www.wikidata.org/wiki/Q225926","display_name":"Kriging","level":2,"score":0.3352000117301941},{"id":"https://openalex.org/C73602740","wikidata":"https://www.wikidata.org/wiki/Q7795822","display_name":"Thompson sampling","level":3,"score":0.31360000371932983},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.30410000681877136},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2939000129699707},{"id":"https://openalex.org/C135320971","wikidata":"https://www.wikidata.org/wiki/Q1868524","display_name":"Local search (optimization)","level":2,"score":0.2937000095844269},{"id":"https://openalex.org/C181789720","wikidata":"https://www.wikidata.org/wiki/Q4812191","display_name":"Asymptotically optimal algorithm","level":2,"score":0.28929999470710754},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.2883000075817108},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.27630001306533813},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27480000257492065},{"id":"https://openalex.org/C189508267","wikidata":"https://www.wikidata.org/wiki/Q17088227","display_name":"Density estimation","level":3,"score":0.2639000117778778},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.2637999951839447},{"id":"https://openalex.org/C2776839635","wikidata":"https://www.wikidata.org/wiki/Q14942679","display_name":"Random tree","level":4,"score":0.26019999384880066}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iros60139.2025.11246858","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros60139.2025.11246858","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W1481216622","https://openalex.org/W1595414374","https://openalex.org/W1763528081","https://openalex.org/W1864112212","https://openalex.org/W1969483458","https://openalex.org/W1971086298","https://openalex.org/W1976930960","https://openalex.org/W1979170109","https://openalex.org/W2107535513","https://openalex.org/W2110762409","https://openalex.org/W2118020555","https://openalex.org/W2128990851","https://openalex.org/W2135003933","https://openalex.org/W2141664020","https://openalex.org/W2161298439","https://openalex.org/W2163634074","https://openalex.org/W2169528473","https://openalex.org/W2171266831","https://openalex.org/W2726539084","https://openalex.org/W2891969900","https://openalex.org/W2963647337","https://openalex.org/W3004407706","https://openalex.org/W3091295346","https://openalex.org/W3199732739","https://openalex.org/W4212795265","https://openalex.org/W4250954493","https://openalex.org/W4283780127","https://openalex.org/W4401416446","https://openalex.org/W4405429268","https://openalex.org/W4405785118","https://openalex.org/W4405786701","https://openalex.org/W4406079894","https://openalex.org/W4409581283","https://openalex.org/W4410530057","https://openalex.org/W4412619192"],"related_works":[],"abstract_inverted_index":{"This":[0],"paper":[1],"introduces":[2],"Context":[3],"Informed":[4,39],"Trees":[5,40],"(CIT*),":[6],"a":[7,109,126,197],"sampling-based":[8],"motion":[9,185],"planning":[10,186],"algorithm":[11],"that":[12,90,98,107,154],"enhances":[13],"exploration":[14,99],"efficiency":[15,147,157],"by":[16,125,158],"biasing":[17],"sampling":[18,85,120,139],"based":[19,36,61],"on":[20,37,62,132,161,196],"uncertainty":[21,73],"estimation":[22,74,78],"from":[23,58],"local":[24,63],"samples":[25],"and":[26,42,51,104,116,148,171,192,206],"connectivity":[27,93],"information":[28],"obtained":[29],"during":[30],"the":[31,84,136],"search":[32,146],"process.":[33],"CIT*":[34,155,178],"is":[35,100,122,209],"Flexible":[38],"(FIT*)":[41],"incorporates":[43],"three":[44],"key":[45],"components:":[46],"region-based":[47],"sampling,":[48,163,177],"uncertainty-driven":[49],"weighting,":[50],"connection-greedy":[52],"prioritization":[53],"(CGP).":[54],"It":[55],"generates":[56],"regions":[57,70,89],"sampled":[59],"states":[60],"obstacle":[64],"proximity,":[65],"assigning":[66],"weights":[67],"to":[68,114,167,190],"these":[69],"using":[71],"probability":[72],"via":[75],"kernel":[76],"density":[77],"(KDE)":[79],"classification.":[80],"To":[81],"further":[82],"refine":[83],"focus,":[86],"CGP":[87],"prioritizes":[88],"exhibit":[91],"strong":[92,180],"in":[94,182,188],"previous":[95],"searches,":[96],"ensuring":[97],"directed":[101],"toward":[102,140],"unknown":[103],"critical":[105,142],"areas":[106],"have":[108],"higher":[110,172],"likelihood":[111],"of":[112,128],"contributing":[113],"feasible":[115],"efficient":[117],"paths.":[118],"The":[119],"process":[121],"then":[123],"guided":[124],"mixture":[127],"Gaussian":[129],"distributions":[130],"centered":[131],"weighted":[133],"regions,":[134,143],"where":[135],"weighting":[137],"biases":[138],"more":[141],"thereby":[144],"improving":[145],"accelerating":[149],"convergence.":[150],"Benchmark":[151],"evaluations":[152],"demonstrate":[153],"improves":[156],"reducing":[159],"reliance":[160],"random":[162],"which":[164],"often":[165],"leads":[166],"slower":[168],"solution":[169],"discovery":[170],"path":[173],"costs.":[174],"With":[175],"biased":[176],"maintains":[179],"performance":[181],"solving":[183],"complex":[184],"problems":[187],"${\\mathbb{R}^4}$":[189],"${\\mathbb{R}^{16}}$":[191],"has":[193],"been":[194],"demonstrated":[195],"real-world":[198],"manipulation":[199],"task.":[200],"A":[201],"video":[202],"showcasing":[203],"our":[204],"method":[205],"experimental":[207],"results":[208],"available":[210],"at:":[211],"https://youtu.be/SG2cy9WmjD0.":[212]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-28T00:00:00"}