{"id":"https://openalex.org/W1997233538","doi":"https://doi.org/10.1109/robot.2010.5509771","title":"An efficient algorithm for on-line determination of collision-free configuration-time points directly from sensor data","display_name":"An efficient algorithm for on-line determination of collision-free configuration-time points directly from sensor data","publication_year":2010,"publication_date":"2010-05-01","ids":{"openalex":"https://openalex.org/W1997233538","doi":"https://doi.org/10.1109/robot.2010.5509771","mag":"1997233538"},"language":"en","primary_location":{"id":"doi:10.1109/robot.2010.5509771","is_oa":false,"landing_page_url":"https://doi.org/10.1109/robot.2010.5509771","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE International Conference on Robotics and Automation","raw_type":"proceedings-article"},"type":"conference-paper","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/A5082716219","display_name":"Rayomand Vatcha","orcid":null},"institutions":[{"id":"https://openalex.org/I102149020","display_name":"University of North Carolina at Charlotte","ror":"https://ror.org/04dawnj30","country_code":"US","type":"education","lineage":["https://openalex.org/I102149020"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rayomand Vatcha","raw_affiliation_strings":["Computer Science, University of North Carolina, Charlotte, USA","[Computer Science,University of North Carolina at Charlotte, USA]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science, University of North Carolina, Charlotte, USA","institution_ids":["https://openalex.org/I102149020"]},{"raw_affiliation_string":"[Computer Science,University of North Carolina at Charlotte, USA]","institution_ids":["https://openalex.org/I102149020"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101669698","display_name":"Jing Xiao","orcid":"https://orcid.org/0000-0002-5675-101X"},"institutions":[{"id":"https://openalex.org/I102149020","display_name":"University of North Carolina at Charlotte","ror":"https://ror.org/04dawnj30","country_code":"US","type":"education","lineage":["https://openalex.org/I102149020"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jing Xiao","raw_affiliation_strings":["Computer Science, University of North Carolina, Charlotte, USA","[Faculty of Computer Science, University of North Carolina at Charlotte, USA]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science, University of North Carolina, Charlotte, USA","institution_ids":["https://openalex.org/I102149020"]},{"raw_affiliation_string":"[Faculty of Computer Science, University of North Carolina at Charlotte, USA]","institution_ids":["https://openalex.org/I102149020"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I102149020"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"25","issue":null,"first_page":"4041","last_page":"4047"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10586","display_name":"Robotic Path Planning Algorithms","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9997000098228455,"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/T10653","display_name":"Robot Manipulation and Learning","score":0.9916999936103821,"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/robot","display_name":"Robot","score":0.6816242933273315},{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.6559532880783081},{"id":"https://openalex.org/keywords/envelope","display_name":"Envelope (radar)","score":0.6504188776016235},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6289205551147461},{"id":"https://openalex.org/keywords/line","display_name":"Line (geometry)","score":0.6217443346977234},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.5054018497467041},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.49922609329223633},{"id":"https://openalex.org/keywords/collision","display_name":"Collision","score":0.475700706243515},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.46560102701187134},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4473768174648285},{"id":"https://openalex.org/keywords/collision-avoidance","display_name":"Collision avoidance","score":0.43537044525146484},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3697577118873596},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.20215356349945068},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13289836049079895}],"concepts":[{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.6816242933273315},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.6559532880783081},{"id":"https://openalex.org/C65155139","wikidata":"https://www.wikidata.org/wiki/Q5380912","display_name":"Envelope (radar)","level":3,"score":0.6504188776016235},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6289205551147461},{"id":"https://openalex.org/C198352243","wikidata":"https://www.wikidata.org/wiki/Q37105","display_name":"Line (geometry)","level":2,"score":0.6217443346977234},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.5054018497467041},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.49922609329223633},{"id":"https://openalex.org/C121704057","wikidata":"https://www.wikidata.org/wiki/Q352070","display_name":"Collision","level":2,"score":0.475700706243515},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.46560102701187134},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4473768174648285},{"id":"https://openalex.org/C2780864053","wikidata":"https://www.wikidata.org/wiki/Q5147495","display_name":"Collision avoidance","level":3,"score":0.43537044525146484},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3697577118873596},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.20215356349945068},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13289836049079895},{"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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/robot.2010.5509771","is_oa":false,"landing_page_url":"https://doi.org/10.1109/robot.2010.5509771","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE International Conference on Robotics and Automation","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4399999976158142,"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":27,"referenced_works":["https://openalex.org/W24904180","https://openalex.org/W101508493","https://openalex.org/W255922872","https://openalex.org/W1503629359","https://openalex.org/W1537452559","https://openalex.org/W1835284335","https://openalex.org/W1839510822","https://openalex.org/W2002440441","https://openalex.org/W2005102720","https://openalex.org/W2045481323","https://openalex.org/W2094539937","https://openalex.org/W2099593662","https://openalex.org/W2111112078","https://openalex.org/W2125503755","https://openalex.org/W2129560912","https://openalex.org/W2131072478","https://openalex.org/W2145104993","https://openalex.org/W2154419175","https://openalex.org/W2155520527","https://openalex.org/W2161675447","https://openalex.org/W2162558751","https://openalex.org/W2167918262","https://openalex.org/W4242811155","https://openalex.org/W6638720791","https://openalex.org/W6638743015","https://openalex.org/W6674861906","https://openalex.org/W6681368648"],"related_works":["https://openalex.org/W2889566344","https://openalex.org/W4317634134","https://openalex.org/W2981729160","https://openalex.org/W2743212448","https://openalex.org/W1819938260","https://openalex.org/W2340892746","https://openalex.org/W626552678","https://openalex.org/W3005999311","https://openalex.org/W607781325","https://openalex.org/W3042530408"],"abstract_inverted_index":{"On-line,":[0],"efficient":[1,21],"perception":[2],"based":[3],"on":[4,136],"sensing":[5,92],"is":[6,24,140],"essential":[7],"for":[8],"an":[9,15],"autonomous":[10],"robot":[11,30,81],"to":[12,26,79,142],"operate":[13],"in":[14],"unknown":[16],"and":[17,36,89,95,130],"unpredictable":[18],"environment.":[19],"An":[20],"on-line":[22,145],"algorithm":[23,110],"introduced":[25],"determine":[27],"whether":[28],"a":[29,32,37,63,83],"at":[31,53,82,107],"future":[33],"time":[34,56,93],"t":[35],"configuration":[38],"q":[39],"will":[40],"be":[41,66,143],"guaranteed":[42],"collision-free,":[43],"directly":[44,102],"from":[45,103],"real-world":[46],"sensor":[47],"data":[48,106],"of":[49],"the":[50,54,70,73,80,90,96,117,122,127,137],"robot's":[51],"environment":[52],"current":[55,91],"\u03c4,":[57,94],"using":[58],"stereo":[59],"vision":[60],"sensor.":[61],"Such":[62],"problem":[64],"can":[65],"formulated":[67],"as":[68,114],"checking":[69],"intersection":[71],"between":[72],"so-called":[74],"dynamic":[75,128],"envelope,":[76],"which":[77,99],"relates":[78],"configuration-time":[84],"(CT)":[85],"point":[86],"(q;":[87],"t)":[88],"atomic":[97,123,134],"obstacles,":[98],"are":[100],"obtained":[101],"low-level":[104],"sensory":[105],"\u03c4.":[108],"The":[109],"achieves":[111],"real-time":[112],"efficiency,":[113],"confirmed":[115],"by":[116,120,131,146],"experimental":[118],"results,":[119],"classifying":[121],"obstacles":[124,135],"possibly":[125],"intersecting":[126],"envelope":[129],"grouping":[132],"relevant":[133],"fly.":[138],"It":[139],"suitable":[141],"used":[144],"sensing-based":[147],"motion":[148],"planners.":[149]},"counts_by_year":[{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
