{"id":"https://openalex.org/W2736357539","doi":"https://doi.org/10.1109/icra.2017.7989097","title":"A layered HMM for predicting motion of a leader in multi-robot settings","display_name":"A layered HMM for predicting motion of a leader in multi-robot settings","publication_year":2017,"publication_date":"2017-05-01","ids":{"openalex":"https://openalex.org/W2736357539","doi":"https://doi.org/10.1109/icra.2017.7989097","mag":"2736357539"},"language":"en","primary_location":{"id":"doi:10.1109/icra.2017.7989097","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2017.7989097","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 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/A5043664098","display_name":"Sina Solaimanpour","orcid":null},"institutions":[{"id":"https://openalex.org/I165733156","display_name":"University of Georgia","ror":"https://ror.org/00te3t702","country_code":"US","type":"education","lineage":["https://openalex.org/I165733156"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Sina Solaimanpour","raw_affiliation_strings":["Department of Computer Science, University of Georgia, Athens, GA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Georgia, Athens, GA, USA","institution_ids":["https://openalex.org/I165733156"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001254145","display_name":"Prashant Doshi","orcid":"https://orcid.org/0000-0001-9042-9131"},"institutions":[{"id":"https://openalex.org/I165733156","display_name":"University of Georgia","ror":"https://ror.org/00te3t702","country_code":"US","type":"education","lineage":["https://openalex.org/I165733156"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Prashant Doshi","raw_affiliation_strings":["Department of Computer Science, University of Georgia, Athens, GA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Georgia, Athens, GA, USA","institution_ids":["https://openalex.org/I165733156"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5043664098"],"corresponding_institution_ids":["https://openalex.org/I165733156"],"apc_list":null,"apc_paid":null,"fwci":1.7552,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.88430355,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"17","issue":null,"first_page":"788","last_page":"793"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9901000261306763,"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"}},"topics":[{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9901000261306763,"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"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.972100019454956,"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"}},{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9638000130653381,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.7903454899787903},{"id":"https://openalex.org/keywords/mobile-robot","display_name":"Mobile robot","score":0.7107598781585693},{"id":"https://openalex.org/keywords/particle-filter","display_name":"Particle filter","score":0.6955054998397827},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.686201274394989},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6621189117431641},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.6406702995300293},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6299469470977783},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.5841500163078308},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5834515690803528},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.583252489566803},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5719440579414368},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5572493672370911},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.4533684551715851},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16816240549087524}],"concepts":[{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.7903454899787903},{"id":"https://openalex.org/C19966478","wikidata":"https://www.wikidata.org/wiki/Q4810574","display_name":"Mobile robot","level":3,"score":0.7107598781585693},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.6955054998397827},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.686201274394989},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6621189117431641},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.6406702995300293},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6299469470977783},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.5841500163078308},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5834515690803528},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.583252489566803},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5719440579414368},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5572493672370911},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.4533684551715851},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16816240549087524},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"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/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icra.2017.7989097","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2017.7989097","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320337345","display_name":"Office of Naval Research","ror":"https://ror.org/00rk2pe57"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1528056001","https://openalex.org/W1581953323","https://openalex.org/W1609461728","https://openalex.org/W1667009210","https://openalex.org/W1674411155","https://openalex.org/W1780392298","https://openalex.org/W2046457978","https://openalex.org/W2047328415","https://openalex.org/W2131865378","https://openalex.org/W2146136606","https://openalex.org/W2152891050","https://openalex.org/W2153353990","https://openalex.org/W2163758305","https://openalex.org/W2215978097","https://openalex.org/W3104119384","https://openalex.org/W4240912299","https://openalex.org/W6634739648","https://openalex.org/W6636452984","https://openalex.org/W6637427424","https://openalex.org/W6679875354","https://openalex.org/W6682242304"],"related_works":["https://openalex.org/W1989212443","https://openalex.org/W2103644279","https://openalex.org/W4302986566","https://openalex.org/W2135362996","https://openalex.org/W1968585197","https://openalex.org/W2539990421","https://openalex.org/W2150578674","https://openalex.org/W877199042","https://openalex.org/W2020716283","https://openalex.org/W1986073206"],"abstract_inverted_index":{"We":[0,94,121],"focus":[1],"on":[2,127],"a":[3,19,45,49,89,96,137],"mobile":[4,139],"robot":[5,64],"that":[6,56],"must":[7],"learn":[8],"another":[9,63],"robot's":[10,68],"motion":[11,90,135,147],"model":[12,91,101,131],"from":[13,115],"observations":[14,69,116],"to":[15,72,132,143],"track":[16],"it":[17],"in":[18,141,152],"given":[20],"map.":[21],"This":[22],"problem":[23,77,104],"has":[24],"several":[25],"real-world":[26],"applications":[27],"such":[28],"as":[29,149],"self-driving":[30],"cars":[31,37],"being":[32],"electronically":[33],"towed":[34],"by":[35],"other":[36],"and":[38,60,105,145],"for":[39,102],"telepresence":[40],"robots.":[41],"Our":[42],"context":[43],"is":[44,92],"nested":[46,74,85],"particle":[47,54],"filter,":[48,55],"generalization":[50],"of":[51,62,83,136],"the":[52,67,76,80,84,112,119,134],"traditional":[53],"allows":[57],"both":[58],"self-localization":[59],"tracking":[61,125],"simultaneously.":[65],"While":[66],"are":[70],"used":[71,151],"weight":[73],"particles,":[75],"arises":[78],"during":[79,87,118],"propagation":[81],"step":[82],"particles":[86],"which":[88,110],"needed.":[93],"introduce":[95],"novel":[97],"layered":[98],"hidden":[99],"Markov":[100],"this":[103,129],"present":[106],"an":[107],"on-line":[108],"algorithm":[109],"learns":[111],"HMM":[113],"parameters":[114],"gathered":[117],"run.":[120],"demonstrate":[122],"significantly":[123],"improved":[124],"accuracy":[126],"using":[128],"new":[130],"predict":[133],"leading":[138],"robot,":[140],"comparison":[142],"pre-defined":[144],"random":[146],"models":[148],"previously":[150],"literature.":[153]},"counts_by_year":[{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
