{"id":"https://openalex.org/W4401018112","doi":"https://doi.org/10.1109/ur61395.2024.10597481","title":"Deep Reinforcement Learning Based Mobile Robot Navigation in Crowd Environments","display_name":"Deep Reinforcement Learning Based Mobile Robot Navigation in Crowd Environments","publication_year":2024,"publication_date":"2024-06-24","ids":{"openalex":"https://openalex.org/W4401018112","doi":"https://doi.org/10.1109/ur61395.2024.10597481"},"language":"en","primary_location":{"id":"doi:10.1109/ur61395.2024.10597481","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ur61395.2024.10597481","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 21st International Conference on Ubiquitous Robots (UR)","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/A5059144839","display_name":"Guang Yang","orcid":"https://orcid.org/0000-0002-3312-5008"},"institutions":[{"id":"https://openalex.org/I108468826","display_name":"Stevens Institute of Technology","ror":"https://ror.org/02z43xh36","country_code":"US","type":"education","lineage":["https://openalex.org/I108468826"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Guang Yang","raw_affiliation_strings":["Stevens Institute of Technology,Department of Electrical and Computer Engineering,Hoboken,NJ,USA,07030"],"affiliations":[{"raw_affiliation_string":"Stevens Institute of Technology,Department of Electrical and Computer Engineering,Hoboken,NJ,USA,07030","institution_ids":["https://openalex.org/I108468826"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082918632","display_name":"Yi Guo","orcid":"https://orcid.org/0000-0002-7158-351X"},"institutions":[{"id":"https://openalex.org/I108468826","display_name":"Stevens Institute of Technology","ror":"https://ror.org/02z43xh36","country_code":"US","type":"education","lineage":["https://openalex.org/I108468826"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yi Guo","raw_affiliation_strings":["Stevens Institute of Technology,Department of Electrical and Computer Engineering,Hoboken,NJ,USA,07030"],"affiliations":[{"raw_affiliation_string":"Stevens Institute of Technology,Department of Electrical and Computer Engineering,Hoboken,NJ,USA,07030","institution_ids":["https://openalex.org/I108468826"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5059144839"],"corresponding_institution_ids":["https://openalex.org/I108468826"],"apc_list":null,"apc_paid":null,"fwci":1.0638,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.74997931,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"513","last_page":"519"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11500","display_name":"Evacuation and Crowd Dynamics","score":0.9851999878883362,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11500","display_name":"Evacuation and Crowd Dynamics","score":0.9851999878883362,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/reinforcement-learning","display_name":"Reinforcement learning","score":0.7845861911773682},{"id":"https://openalex.org/keywords/mobile-robot","display_name":"Mobile robot","score":0.7518903613090515},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7272997498512268},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5562962293624878},{"id":"https://openalex.org/keywords/mobile-robot-navigation","display_name":"Mobile robot navigation","score":0.452388733625412},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.42859140038490295},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4134776294231415},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.3897043764591217},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.33247992396354675},{"id":"https://openalex.org/keywords/robot-control","display_name":"Robot control","score":0.28332221508026123}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7845861911773682},{"id":"https://openalex.org/C19966478","wikidata":"https://www.wikidata.org/wiki/Q4810574","display_name":"Mobile robot","level":3,"score":0.7518903613090515},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7272997498512268},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5562962293624878},{"id":"https://openalex.org/C26990112","wikidata":"https://www.wikidata.org/wiki/Q6887224","display_name":"Mobile robot navigation","level":5,"score":0.452388733625412},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.42859140038490295},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4134776294231415},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.3897043764591217},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.33247992396354675},{"id":"https://openalex.org/C65401140","wikidata":"https://www.wikidata.org/wiki/Q7353385","display_name":"Robot control","level":4,"score":0.28332221508026123}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ur61395.2024.10597481","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ur61395.2024.10597481","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 21st International Conference on Ubiquitous Robots (UR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G450425986","display_name":null,"funder_award_id":"CMMI-1825709,IIS-1838799","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1969483458","https://openalex.org/W2077638917","https://openalex.org/W2117211893","https://openalex.org/W2121204166","https://openalex.org/W2145089737","https://openalex.org/W2167052694","https://openalex.org/W2534986651","https://openalex.org/W2604216058","https://openalex.org/W2890001928","https://openalex.org/W2910123761","https://openalex.org/W2962954724","https://openalex.org/W2963428623","https://openalex.org/W2963809389","https://openalex.org/W3026389972","https://openalex.org/W3112757086","https://openalex.org/W3129678734","https://openalex.org/W3130634520","https://openalex.org/W3131482536","https://openalex.org/W3207293238","https://openalex.org/W4226150156","https://openalex.org/W4229377071","https://openalex.org/W4285047690","https://openalex.org/W4385488417","https://openalex.org/W6681425221","https://openalex.org/W6684921986"],"related_works":["https://openalex.org/W2122871747","https://openalex.org/W2093507260","https://openalex.org/W2562628082","https://openalex.org/W3114279067","https://openalex.org/W2378318959","https://openalex.org/W2083994890","https://openalex.org/W2369541203","https://openalex.org/W2360994936","https://openalex.org/W3081536868","https://openalex.org/W2523265608"],"abstract_inverted_index":{"Robots":[0],"are":[1,97],"becoming":[2],"popular":[3],"in":[4,11,88,101,160],"assisting":[5],"humans.":[6],"The":[7,93],"mobile":[8,25],"robot":[9,26,66],"navigation":[10,27],"human":[12],"crowd":[13],"environments":[14],"has":[15,168],"become":[16],"more":[17],"important.":[18],"We":[19,71,104],"propose":[20],"a":[21,73,89,117,126],"deep":[22,51],"reinforcement":[23],"learning-based":[24],"method":[28,56,111,155,167],"that":[29,59,85,115,153],"takes":[30],"the":[31,34,43,47,65,68,106,134,140,174],"observation":[32],"from":[33],"robot's":[35],"onboard":[36],"Lidar":[37],"sensor":[38],"as":[39],"input":[40],"and":[41,99,125,145,163],"outputs":[42],"velocity":[44],"control":[45],"to":[46,63],"robot.":[48],"A":[49],"customized":[50],"deterministic":[52],"policy":[53],"gradient":[54],"(DDPG)":[55],"is":[57],"developed":[58],"incorporates":[60],"guiding":[61],"points":[62],"guide":[64],"toward":[67],"global":[69],"goal.":[70],"built":[72],"3D":[74],"simulation":[75,150],"environment":[76],"using":[77],"an":[78,121],"open":[79],"dataset":[80],"of":[81,108,137,142],"real-world":[82],"pedestrian":[83],"trajectories":[84],"were":[86],"collected":[87],"large":[90],"business":[91],"center.":[92],"neural":[94],"network":[95],"models":[96],"trained":[98,162],"tested":[100],"such":[102],"environments.":[103,165],"compare":[105],"performance":[107],"our":[109,154],"proposed":[110],"with":[112,173],"existing":[113,122],"algorithms":[114],"include":[116],"classic":[118],"motion":[119],"planner,":[120],"DDPG":[123],"method,":[124],"generative":[127],"adversarial":[128],"imitation":[129],"learning":[130],"(GAIL)":[131],"method.":[132,176],"Using":[133],"measurement":[135],"metrics":[136],"success":[138],"rate,":[139],"number":[141],"times":[143],"freezing,":[144],"normalized":[146],"path":[147],"length,":[148],"extensive":[149],"results":[151],"show":[152],"outperforms":[156],"other":[157],"state-of-the-art":[158],"approaches":[159],"both":[161],"untrained":[164],"Our":[166],"also":[169],"better":[170],"generalizability":[171],"compared":[172],"GAIL":[175]},"counts_by_year":[{"year":2025,"cited_by_count":3}],"updated_date":"2025-12-26T23:08:49.675405","created_date":"2025-10-10T00:00:00"}