{"id":"https://openalex.org/W2026680900","doi":"https://doi.org/10.1163/15685530260425738","title":"Sensor planning for mobile robot localization using Bayesian network inference","display_name":"Sensor planning for mobile robot localization using Bayesian network inference","publication_year":2002,"publication_date":"2002-01-01","ids":{"openalex":"https://openalex.org/W2026680900","doi":"https://doi.org/10.1163/15685530260425738","mag":"2026680900"},"language":"en","primary_location":{"id":"doi:10.1163/15685530260425738","is_oa":false,"landing_page_url":"https://doi.org/10.1163/15685530260425738","pdf_url":null,"source":{"id":"https://openalex.org/S192584203","display_name":"Advanced Robotics","issn_l":"0169-1864","issn":["0169-1864","1568-5535"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Advanced Robotics","raw_type":"journal-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/A5101789136","display_name":"Hongjun Zhou","orcid":"https://orcid.org/0000-0002-6256-2485"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hongjun Zhou","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5026336262","display_name":"Shigeyuki Sakane","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shigeyuki Sakane","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.1063,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.84543843,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"16","issue":"8","first_page":"751","last_page":"771"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9975000023841858,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9975000023841858,"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/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.995199978351593,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9944999814033508,"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/mobile-robot","display_name":"Mobile robot","score":0.7256979942321777},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6430003046989441},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6126182079315186},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5974335670471191},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.5842204093933105},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.5383034944534302},{"id":"https://openalex.org/keywords/mobile-robot-navigation","display_name":"Mobile robot navigation","score":0.5258377194404602},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.5026109218597412},{"id":"https://openalex.org/keywords/dynamic-bayesian-network","display_name":"Dynamic Bayesian network","score":0.4531332850456238},{"id":"https://openalex.org/keywords/wireless-sensor-network","display_name":"Wireless sensor network","score":0.43454283475875854},{"id":"https://openalex.org/keywords/motion-planning","display_name":"Motion planning","score":0.4309229254722595},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.3887612521648407},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37955042719841003},{"id":"https://openalex.org/keywords/robot-control","display_name":"Robot control","score":0.22840026021003723}],"concepts":[{"id":"https://openalex.org/C19966478","wikidata":"https://www.wikidata.org/wiki/Q4810574","display_name":"Mobile robot","level":3,"score":0.7256979942321777},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6430003046989441},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6126182079315186},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5974335670471191},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.5842204093933105},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.5383034944534302},{"id":"https://openalex.org/C26990112","wikidata":"https://www.wikidata.org/wiki/Q6887224","display_name":"Mobile robot navigation","level":5,"score":0.5258377194404602},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.5026109218597412},{"id":"https://openalex.org/C82142266","wikidata":"https://www.wikidata.org/wiki/Q3456604","display_name":"Dynamic Bayesian network","level":3,"score":0.4531332850456238},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.43454283475875854},{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.4309229254722595},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3887612521648407},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37955042719841003},{"id":"https://openalex.org/C65401140","wikidata":"https://www.wikidata.org/wiki/Q7353385","display_name":"Robot control","level":4,"score":0.22840026021003723},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1163/15685530260425738","is_oa":false,"landing_page_url":"https://doi.org/10.1163/15685530260425738","pdf_url":null,"source":{"id":"https://openalex.org/S192584203","display_name":"Advanced Robotics","issn_l":"0169-1864","issn":["0169-1864","1568-5535"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Advanced Robotics","raw_type":"journal-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.74.1503","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.74.1503","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.indsys.chuo-u.ac.jp/~zhou/paper/iros02.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.80.4853","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.80.4853","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.indsys.chuo-u.ac.jp/~zhou/paper/iros02.ps.gz","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.6399999856948853}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1513861746","https://openalex.org/W1582470588","https://openalex.org/W1585719420","https://openalex.org/W1592813209","https://openalex.org/W1928061009","https://openalex.org/W1932381787","https://openalex.org/W2052305127","https://openalex.org/W2079344866","https://openalex.org/W2159080219","https://openalex.org/W2171265988","https://openalex.org/W4300313336","https://openalex.org/W4301208911"],"related_works":["https://openalex.org/W2157678966","https://openalex.org/W2578973671","https://openalex.org/W2215058820","https://openalex.org/W2945000716","https://openalex.org/W2097663773","https://openalex.org/W1602184117","https://openalex.org/W2564827943","https://openalex.org/W2511198839","https://openalex.org/W1966557338","https://openalex.org/W2366931106"],"abstract_inverted_index":{"We":[0,171],"propose":[1],"a":[2,34,63,83,102,135,147],"new":[3],"method":[4],"of":[5,24,33,42,75,153,164],"sensor":[6,50,77,87,95,116,129,183],"planning":[7,51,184],"for":[8,45,66,86,97],"mobile":[9,90,125],"robot":[10,91,126,178],"localization":[11,98,166],"using":[12,101],"Bayesian":[13,35,84,136],"network":[14,44,85],"inference.":[15],"Since":[16,106],"we":[17,37,61],"can":[18,38],"model":[19],"causal":[20],"relations":[21],"between":[22],"situations":[23],"the":[25,40,43,80,93,107,112,115,121,124,154,165,168,182],"robot's":[26],"behavior":[27],"and":[28,52,68,79,99,114,133,138,167,176],"sensing":[29,56,143,169],"events":[30],"as":[31],"nodes":[32],"network,":[36,137],"use":[39],"inference":[41,81,152],"dealing":[46],"with":[47],"uncertainty":[48],"in":[49,120],"thus":[53],"derive":[54],"appropriate":[55,142],"actions.":[57],"In":[58],"this":[59],"system":[60],"employ":[62],"multi-layered-behavior":[64],"architecture":[65,71],"navigation":[67,100,113],"localization.":[69],"This":[70],"effectively":[72],"combines":[73],"mapping":[74],"local":[76,94],"information":[78,130],"via":[82],"planning.":[88],"The":[89,157],"recognizes":[92],"patterns":[96],"learned":[103],"regression":[104],"function.":[105],"environment":[108],"may":[109],"change":[110],"during":[111],"capability":[117],"has":[118],"limitations":[119],"real":[122,177],"world,":[123],"actively":[127],"gathers":[128],"to":[131,180],"construct":[132],"reconstruct":[134],"then":[139],"derives":[140],"an":[141],"action":[144],"which":[145],"maximizes":[146],"utility":[148,158],"function":[149,159],"based":[150],"on":[151],"reconstructed":[155],"network.":[156],"takes":[160],"into":[161],"account":[162],"belief":[163],"cost.":[170],"have":[172],"conducted":[173],"some":[174],"simulation":[175],"experiments":[179],"validate":[181],"system.":[185]},"counts_by_year":[{"year":2015,"cited_by_count":1},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":1}],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2025-10-10T00:00:00"}