{"id":"https://openalex.org/W1480152866","doi":"https://doi.org/10.1109/icra.2015.7139246","title":"Distance metric learning for RRT-based motion planning with constant-time inference","display_name":"Distance metric learning for RRT-based motion planning with constant-time inference","publication_year":2015,"publication_date":"2015-05-01","ids":{"openalex":"https://openalex.org/W1480152866","doi":"https://doi.org/10.1109/icra.2015.7139246","mag":"1480152866"},"language":"en","primary_location":{"id":"doi:10.1109/icra.2015.7139246","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2015.7139246","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Robotics and Automation (ICRA)","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/A5017989436","display_name":"Luigi Palmieri","orcid":"https://orcid.org/0000-0002-4908-5434"},"institutions":[{"id":"https://openalex.org/I161046081","display_name":"University of Freiburg","ror":"https://ror.org/0245cg223","country_code":"DE","type":"education","lineage":["https://openalex.org/I161046081"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Luigi Palmieri","raw_affiliation_strings":["Dept. of Computer Science, University of Freiburg, Germany","Social Robotics Lab, Department of Computer Science, University of Freiburg, Germany"],"affiliations":[{"raw_affiliation_string":"Dept. of Computer Science, University of Freiburg, Germany","institution_ids":["https://openalex.org/I161046081"]},{"raw_affiliation_string":"Social Robotics Lab, Department of Computer Science, University of Freiburg, Germany","institution_ids":["https://openalex.org/I161046081"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111969299","display_name":"Kai O. Arras","orcid":null},"institutions":[{"id":"https://openalex.org/I161046081","display_name":"University of Freiburg","ror":"https://ror.org/0245cg223","country_code":"DE","type":"education","lineage":["https://openalex.org/I161046081"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Kai O. Arras","raw_affiliation_strings":["Dept. of Computer Science, University of Freiburg, Germany","Social Robotics Lab, Department of Computer Science, University of Freiburg, Germany"],"affiliations":[{"raw_affiliation_string":"Dept. of Computer Science, University of Freiburg, Germany","institution_ids":["https://openalex.org/I161046081"]},{"raw_affiliation_string":"Social Robotics Lab, Department of Computer Science, University of Freiburg, Germany","institution_ids":["https://openalex.org/I161046081"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5017989436"],"corresponding_institution_ids":["https://openalex.org/I161046081"],"apc_list":null,"apc_paid":null,"fwci":3.6819,"has_fulltext":false,"cited_by_count":44,"citation_normalized_percentile":{"value":0.95314986,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"637","last_page":"643"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10586","display_name":"Robotic Path Planning Algorithms","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9787999987602234,"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/T10571","display_name":"Robotic Mechanisms and Dynamics","score":0.9787999987602234,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/motion-planning","display_name":"Motion planning","score":0.689190149307251},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6711666584014893},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5464916229248047},{"id":"https://openalex.org/keywords/euclidean-distance","display_name":"Euclidean distance","score":0.5105112791061401},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45601987838745117},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.4535660445690155},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.43621814250946045},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4313640594482422},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4280097186565399},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.4131866991519928},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.36535054445266724},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.30073094367980957},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.13214123249053955},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09676143527030945}],"concepts":[{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.689190149307251},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6711666584014893},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5464916229248047},{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.5105112791061401},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45601987838745117},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.4535660445690155},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.43621814250946045},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4313640594482422},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4280097186565399},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.4131866991519928},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.36535054445266724},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.30073094367980957},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.13214123249053955},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09676143527030945},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icra.2015.7139246","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2015.7139246","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7799999713897705,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320338370","display_name":"FP7 Information and Communication Technologies","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1763528081","https://openalex.org/W1777783943","https://openalex.org/W1964307222","https://openalex.org/W1971086298","https://openalex.org/W1983764986","https://openalex.org/W1985368783","https://openalex.org/W1995831026","https://openalex.org/W2000194319","https://openalex.org/W2031335381","https://openalex.org/W2054585537","https://openalex.org/W2101902009","https://openalex.org/W2151967815","https://openalex.org/W2154574107","https://openalex.org/W2161920802","https://openalex.org/W2165307897","https://openalex.org/W2166077797","https://openalex.org/W2171205311","https://openalex.org/W2911964244","https://openalex.org/W2949623849","https://openalex.org/W4247382745","https://openalex.org/W6638062875"],"related_works":["https://openalex.org/W2188500270","https://openalex.org/W2303858293","https://openalex.org/W2915512527","https://openalex.org/W2055243143","https://openalex.org/W2793336762","https://openalex.org/W2091548507","https://openalex.org/W51364034","https://openalex.org/W2141938446","https://openalex.org/W2898073868","https://openalex.org/W4284663758"],"abstract_inverted_index":{"The":[0,112],"distance":[1,40,97],"metric":[2,41],"is":[3,73,115],"a":[4,34,47,64],"key":[5],"component":[6],"in":[7,79,120],"RRT-based":[8,43],"motion":[9],"planning":[10,22,30,121],"that":[11,72],"deeply":[12],"affects":[13],"coverage":[14],"of":[15,81,108,129],"the":[16,25,39,53,106],"state":[17],"space,":[18],"path":[19,130],"quality":[20],"and":[21,83,104],"time.":[23],"With":[24],"goal":[26],"to":[27,37,75,94,117],"speed":[28],"up":[29],"time,":[31],"we":[32,62,90],"introduce":[33],"learning":[35,113],"approach":[36,93,114],"approximate":[38],"for":[42,58],"planners.":[44],"By":[45],"exploiting":[46],"novel":[48],"steer":[49],"function":[50],"which":[51],"solves":[52],"two-point":[54],"boundary":[55],"value":[56],"problem":[57],"wheeled":[59],"mobile":[60],"robots,":[61],"train":[63],"simple":[65],"nonlinear":[66],"parametric":[67],"model":[68],"with":[69],"constant-time":[70],"inference":[71],"shown":[74,116],"predict":[76],"distances":[77],"accurately":[78],"terms":[80],"regression":[82,102],"ranking":[84],"performance.":[85],"In":[86],"an":[87,95],"extensive":[88],"analysis":[89],"compare":[91],"our":[92],"Euclidean":[96],"baseline,":[98],"consider":[99],"four":[100],"alternative":[101],"models":[103],"study":[105],"impact":[107],"domain-specific":[109],"feature":[110],"expansion.":[111],"be":[118],"faster":[119],"time":[122],"by":[123],"several":[124],"factors":[125],"at":[126],"negligible":[127],"loss":[128],"quality.":[131]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":6},{"year":2017,"cited_by_count":5},{"year":2016,"cited_by_count":9}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}