iBet uBet
web content aggregator. Adding the entire web to your favor.
Link to original content:
https://api.openalex.org/works/doi:10.48550/ARXIV.2209.00296
{"id":"https://openalex.org/W4294530334","doi":"https://doi.org/10.48550/arxiv.2209.00296","title":"Monocular Camera-based Complex Obstacle Avoidance via Efficient Deep Reinforcement Learning","display_name":"Monocular Camera-based Complex Obstacle Avoidance via Efficient Deep Reinforcement Learning","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4294530334","doi":"https://doi.org/10.48550/arxiv.2209.00296"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2209.00296","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false},"type":"preprint","type_crossref":"posted-content","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/abs/2209.00296","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5044995947","display_name":"Jianchuan Ding","orcid":"https://orcid.org/0000-0003-1890-6903"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ding, Jianchuan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048365677","display_name":"Lingping Gao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Lingping","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045303340","display_name":"Wenxi Liu","orcid":"https://orcid.org/0000-0002-3630-6322"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Wenxi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004540162","display_name":"Haiyin Piao","orcid":"https://orcid.org/0000-0002-8519-4750"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Piao, Haiyin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076812698","display_name":"Jia Pan","orcid":"https://orcid.org/0000-0001-9003-2054"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pan, Jia","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100887725","display_name":"Zhenjun Du","orcid":"https://orcid.org/0000-0002-4791-4630"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Du, Zhenjun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068792201","display_name":"Xin Yang","orcid":"https://orcid.org/0000-0002-8046-722X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Xin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5020527092","display_name":"Baocai Yin","orcid":"https://orcid.org/0000-0003-3121-1823"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yin, Baocai","raw_affiliation_strings":[],"affiliations":[]}],"institution_assertions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":0,"max":60},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9983,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9983,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/T10036","display_name":"Advanced Neural Network Applications","score":0.9769,"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/T10586","display_name":"Robotic Path Planning Algorithms","score":0.965,"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/robustness","display_name":"Robustness","score":0.78240037},{"id":"https://openalex.org/keywords/obstacle-avoidance","display_name":"Obstacle avoidance","score":0.73745465},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.61683774},{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.6159737}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.78240037},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.75911677},{"id":"https://openalex.org/C6683253","wikidata":"https://www.wikidata.org/wiki/Q7075535","display_name":"Obstacle avoidance","level":4,"score":0.73745465},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7045476},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6985568},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6463754},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.61683774},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.6159737},{"id":"https://openalex.org/C2776650193","wikidata":"https://www.wikidata.org/wiki/Q264661","display_name":"Obstacle","level":2,"score":0.5996875},{"id":"https://openalex.org/C2780864053","wikidata":"https://www.wikidata.org/wiki/Q5147495","display_name":"Collision avoidance","level":3,"score":0.5204242},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.4841457},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.43695614},{"id":"https://openalex.org/C19966478","wikidata":"https://www.wikidata.org/wiki/Q4810574","display_name":"Mobile robot","level":3,"score":0.14966008},{"id":"https://openalex.org/C121704057","wikidata":"https://www.wikidata.org/wiki/Q352070","display_name":"Collision","level":2,"score":0.14847144},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"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/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2209.00296","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false},{"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2209.00296","pdf_url":"http://arxiv.org/pdf/2209.00296","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},{"is_oa":false,"landing_page_url":"https://api.datacite.org/dois/10.48550/arxiv.2209.00296","pdf_url":null,"source":{"id":"https://openalex.org/S4393179698","display_name":"DataCite API","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210145204","host_organization_name":"DataCite","host_organization_lineage":["https://openalex.org/I4210145204"],"host_organization_lineage_names":["DataCite"],"type":"metadata"},"license":null,"license_id":null,"version":null}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2209.00296","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4391249562","https://openalex.org/W4253519380","https://openalex.org/W3043170174","https://openalex.org/W2930076404","https://openalex.org/W2913749762","https://openalex.org/W2782776446","https://openalex.org/W2596413128","https://openalex.org/W2356867392","https://openalex.org/W2321940404","https://openalex.org/W2071957557"],"abstract_inverted_index":{"Deep":[0],"reinforcement":[1,113],"learning":[2],"has":[3,177],"achieved":[4],"great":[5],"success":[6],"in":[7],"laser-based":[8],"collision":[9],"avoidance":[10,97,197],"works":[11],"because":[12],"the":[13,28,31,38,42,67,103,117,134,142,148,170,175,182,190,195],"laser":[14,47,119],"can":[15,26],"sense":[16],"accurate":[17],"depth":[18,143],"information":[19,131,146],"without":[20],"too":[21],"much":[22],"redundant":[23],"data,":[24],"which":[25,152,185],"maintain":[27],"robustness":[29,65],"of":[30,59,147,194],"algorithm":[32],"when":[33],"it":[34],"is":[35,186],"migrated":[36],"from":[37,133],"simulation":[39],"environment":[40],"to":[41,53,107,116,168,188],"real":[43],"world.":[44],"However,":[45],"high-cost":[46],"devices":[48],"are":[49],"not":[50],"only":[51,127],"difficult":[52],"deploy":[54],"for":[55,110,157,181],"a":[56,91,123,163],"large":[57],"scale":[58],"robots":[60],"but":[61],"also":[62,161],"demonstrate":[63],"unsatisfactory":[64],"towards":[66],"complex":[68,81,95,158],"obstacles,":[69,72,136],"including":[70],"irregular":[71],"e.g.,":[73],"tables,":[74],"chairs,":[75],"and":[76,83,144,174,192],"shelves,":[77],"as":[78,80],"well":[79],"ground":[82],"special":[84],"materials.":[85],"In":[86],"this":[87],"paper,":[88],"we":[89,100],"propose":[90],"novel":[92],"monocular":[93],"camera-based":[94],"obstacle":[96,196],"framework.":[98],"Particularly,":[99],"innovatively":[101],"transform":[102],"captured":[104,121,149],"RGB":[105,150],"images":[106],"pseudo-laser":[108,139,172],"measurements":[109],"efficient":[111],"deep":[112],"learning.":[114],"Compared":[115],"traditional":[118],"measurement":[120,140],"at":[122],"certain":[124],"height":[125],"that":[126],"contains":[128],"one-dimensional":[129],"distance":[130],"away":[132],"neighboring":[135],"our":[137,154],"proposed":[138],"fuses":[141],"semantic":[145],"image,":[151],"makes":[153],"method":[155],"effective":[156],"obstacles.":[159],"We":[160],"design":[162],"feature":[164],"extraction":[165],"guidance":[166],"module":[167],"weight":[169],"input":[171],"measurement,":[173],"agent":[176],"more":[178],"reasonable":[179],"attention":[180],"current":[183],"state,":[184],"conducive":[187],"improving":[189],"accuracy":[191],"efficiency":[193],"policy.":[198]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4294530334","counts_by_year":[],"updated_date":"2024-12-06T01:48:18.937020","created_date":"2022-09-03"}
