{"id":"https://openalex.org/W4407474010","doi":"https://doi.org/10.1109/cac63892.2024.10864990","title":"DDQN-Based Path Planning for Mining Cave Robots","display_name":"DDQN-Based Path Planning for Mining Cave Robots","publication_year":2024,"publication_date":"2024-11-01","ids":{"openalex":"https://openalex.org/W4407474010","doi":"https://doi.org/10.1109/cac63892.2024.10864990"},"language":"en","primary_location":{"id":"doi:10.1109/cac63892.2024.10864990","doi":"https://doi.org/10.1109/cac63892.2024.10864990","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cac63892.2024.10864990","pdf_url":null,"source":{"id":"https://openalex.org/S4363604230","display_name":"China Automation Congress (CAC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 China Automation Congress (CAC)","raw_type":"proceedings-article"},"volume":null,"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":null,"display_name":"Fei Du","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fei Du","raw_affiliation_strings":["School of Automation, China University of Geosciences,Wuhan,China,430074"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation, China University of Geosciences,Wuhan,China,430074","institution_ids":["https://openalex.org/I3124059619"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101616350","display_name":"Xinmei Wang","orcid":"https://orcid.org/0009-0002-2131-5741","observed_orcids":["https://orcid.org/0009-0002-2131-5741"]},"institutions":[{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinmei Wang","raw_affiliation_strings":["School of Automation, China University of Geosciences,Wuhan,China,430074"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation, China University of Geosciences,Wuhan,China,430074","institution_ids":["https://openalex.org/I3124059619"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I3124059619"],"apc_list":null,"apc_paid":null,"fwci":0.3171,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.57808252,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"234","last_page":"239"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.5156000256538391,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.5156000256538391,"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/T10586","display_name":"Robotic Path Planning Algorithms","score":0.4787999987602234,"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/T11574","display_name":"Artificial Intelligence in Games","score":0.0010999999940395355,"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/double-deep-q-network","display_name":"double deep Q-network","score":0.8500000238418579},{"id":"https://openalex.org/keywords/path-planning","display_name":"path planning","score":0.9300000071525574},{"id":"https://openalex.org/keywords/deep-reinforcement-learning","display_name":"deep reinforcement learning","score":0.6600000262260437},{"id":"https://openalex.org/keywords/q-learning","display_name":"Q-learning","score":0.7789999842643738},{"id":"https://openalex.org/keywords/deep-q-network","display_name":"deep Q-network","score":0.8700000047683716},{"id":"https://openalex.org/keywords/experience-replay","display_name":"experience replay","score":0.9610000252723694},{"id":"https://openalex.org/keywords/exploration-strategy","display_name":"exploration strategy","score":0.7839999794960022},{"id":"https://openalex.org/keywords/unknown-environments","display_name":"unknown environments","score":0.7879999876022339},{"id":"https://openalex.org/keywords/convergence-rate","display_name":"convergence rate","score":0.4950000047683716}],"concepts":[{"id":"https://openalex.org/C171878925","wikidata":"https://www.wikidata.org/wiki/Q35509","display_name":"Cave","level":2,"score":0.7224445939064026},{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.6863550543785095},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.6540646553039551},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6401353478431702},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.5445017218589783},{"id":"https://openalex.org/C19966478","wikidata":"https://www.wikidata.org/wiki/Q4810574","display_name":"Mobile robot","level":3,"score":0.4318584203720093},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31298261880874634},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.1663852334022522},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.11701378226280212},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0963316261768341}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cac63892.2024.10864990","doi":"https://doi.org/10.1109/cac63892.2024.10864990","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cac63892.2024.10864990","pdf_url":null,"source":{"id":"https://openalex.org/S4363604230","display_name":"China Automation Congress (CAC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 China Automation Congress (CAC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W32403112","https://openalex.org/W1969483458","https://openalex.org/W2103120971","https://openalex.org/W2117211893","https://openalex.org/W2145339207","https://openalex.org/W2169528473","https://openalex.org/W2781786131","https://openalex.org/W3206972316","https://openalex.org/W4388206150","https://openalex.org/W4394596049","https://openalex.org/W6685444567","https://openalex.org/W6747473740","https://openalex.org/W6780995382"],"related_works":["https://openalex.org/W2122871747","https://openalex.org/W1904098742","https://openalex.org/W3158921809","https://openalex.org/W1980828932","https://openalex.org/W2161428574","https://openalex.org/W3114279067","https://openalex.org/W1972453571","https://openalex.org/W2077416514","https://openalex.org/W2618632915","https://openalex.org/W2359600231"],"abstract_inverted_index":{"This":[0],"study":[1],"aims":[2],"to":[3,29,65,112],"address":[4],"the":[5,18,37,41,67,98,114,123,135],"path":[6,21,44,52,74,101,150],"planning":[7,45,53],"problem":[8],"in":[9,50,55,73,127,149,157],"complex":[10,60,158],"mining":[11,61],"cave":[12],"environments,":[13],"with":[14,120],"DDQN":[15,38,71,80,118,125,137],"chosen":[16],"as":[17,59,86],"algorithm":[19,39,81,138],"for":[20],"planning.":[22,102],"Firstly,":[23],"we":[24,35,76,105],"provide":[25],"a":[26,107],"detailed":[27],"introduction":[28],"Q-learning,":[30],"DQN,":[31],"and":[32,46,92,145,155],"DDQN.":[33],"Subsequently,":[34],"introduce":[36],"into":[40],"field":[42],"of":[43,69,100,109,116,122],"explore":[47],"its":[48,153],"potential":[49],"solving":[51],"problems":[54],"unknown":[56],"environments":[57],"such":[58,85],"caves.":[62],"In":[63],"order":[64],"overcome":[66],"limitations":[68],"traditional":[70,117],"algorithms":[72,119,126],"planning,":[75,151],"propose":[77],"an":[78],"improved":[79,124,136],"by":[82],"introducing":[83],"techniques":[84],"experience":[87],"replay,":[88],"target":[89],"network":[90],"update,":[91],"exploration":[93],"strategy":[94],"optimization,":[95],"thereby":[96],"enhancing":[97],"effectiveness":[99,154],"Following":[103],"this,":[104],"conduct":[106],"series":[108],"simulation":[110],"experiments":[111],"compare":[113],"performance":[115],"that":[121,134],"various":[128],"scenarios.":[129],"The":[130],"experimental":[131],"results":[132],"demonstrate":[133],"exhibits":[139],"faster":[140],"convergence,":[141],"more":[142],"stable":[143],"performance,":[144],"higher":[146],"reward":[147],"values":[148],"confirming":[152],"superiority":[156],"environments.":[159]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-10-05T09:50:58.205359","created_date":"2025-10-10T00:00:00"}