{"id":"https://openalex.org/W2955087651","doi":"https://doi.org/10.1109/lra.2019.2924839","title":"DeepIG: Multi-Robot Information Gathering With Deep Reinforcement Learning","display_name":"DeepIG: Multi-Robot Information Gathering With Deep Reinforcement Learning","publication_year":2019,"publication_date":"2019-06-24","ids":{"openalex":"https://openalex.org/W2955087651","doi":"https://doi.org/10.1109/lra.2019.2924839","mag":"2955087651"},"language":"en","primary_location":{"id":"doi:10.1109/lra.2019.2924839","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lra.2019.2924839","pdf_url":null,"source":{"id":"https://openalex.org/S4210169774","display_name":"IEEE Robotics and Automation Letters","issn_l":"2377-3766","issn":["2377-3766","2377-3774"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Robotics and Automation Letters","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/A5059112748","display_name":"Alberto Viseras","orcid":"https://orcid.org/0000-0001-5219-6533"},"institutions":[{"id":"https://openalex.org/I2898391981","display_name":"Deutsches Zentrum f\u00fcr Luft- und Raumfahrt e. V. (DLR)","ror":"https://ror.org/04bwf3e34","country_code":"DE","type":"facility","lineage":["https://openalex.org/I1305996414","https://openalex.org/I2898391981"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Alberto Viseras","raw_affiliation_strings":["Institute of Communications and Navigation of the German Aerospace Center (DLR), Oberpfaffenhofen, Germany"],"raw_orcid":"https://orcid.org/0000-0001-5219-6533","affiliations":[{"raw_affiliation_string":"Institute of Communications and Navigation of the German Aerospace Center (DLR), Oberpfaffenhofen, Germany","institution_ids":["https://openalex.org/I2898391981"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059021441","display_name":"Ricardo Garc\u00eda","orcid":"https://orcid.org/0000-0002-2553-7272"},"institutions":[{"id":"https://openalex.org/I88060688","display_name":"Universidad Polit\u00e9cnica de Madrid","ror":"https://ror.org/03n6nwv02","country_code":"ES","type":"education","lineage":["https://openalex.org/I88060688"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Ricardo Garcia","raw_affiliation_strings":["E.T.S.I. Telecomunicaci\u00f3n, Universidad Polit\u00e9cnica de Madrid, Madrid, Spain"],"raw_orcid":"https://orcid.org/0000-0002-2553-7272","affiliations":[{"raw_affiliation_string":"E.T.S.I. Telecomunicaci\u00f3n, Universidad Polit\u00e9cnica de Madrid, Madrid, Spain","institution_ids":["https://openalex.org/I88060688"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.553,"has_fulltext":false,"cited_by_count":36,"citation_normalized_percentile":{"value":0.91504096,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"4","issue":"3","first_page":"3059","last_page":"3066"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9976999759674072,"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":0.9976999759674072,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9944999814033508,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9908999800682068,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/computer-science","display_name":"Computer science","score":0.8144925236701965},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.7824708223342896},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7146041393280029},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.7098575830459595},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5921894907951355},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5664073824882507},{"id":"https://openalex.org/keywords/terrain","display_name":"Terrain","score":0.5247802138328552},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4907778203487396},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.4653419256210327},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4350530505180359},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.35533109307289124}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8144925236701965},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7824708223342896},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7146041393280029},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.7098575830459595},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5921894907951355},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5664073824882507},{"id":"https://openalex.org/C161840515","wikidata":"https://www.wikidata.org/wiki/Q186131","display_name":"Terrain","level":2,"score":0.5247802138328552},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4907778203487396},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.4653419256210327},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4350530505180359},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35533109307289124},{"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/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/lra.2019.2924839","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lra.2019.2924839","pdf_url":null,"source":{"id":"https://openalex.org/S4210169774","display_name":"IEEE Robotics and Automation Letters","issn_l":"2377-3766","issn":["2377-3766","2377-3774"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Robotics and Automation Letters","raw_type":"journal-article"},{"id":"pmh:oai:elib.dlr.de:127044","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4377196266","display_name":"elib (German Aerospace Center)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2898391981","host_organization_name":"Deutsches Zentrum f\u00fcr Luft- und Raumfahrt e. V. (DLR)","host_organization_lineage":["https://openalex.org/I2898391981"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"","raw_type":"Konferenzbeitrag"},{"id":"pmh:oai:elib.dlr.de:132180","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lra.2019.2924839>.","pdf_url":null,"source":{"id":"https://openalex.org/S4377196266","display_name":"elib (German Aerospace Center)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2898391981","host_organization_name":"Deutsches Zentrum f\u00fcr Luft- und Raumfahrt e. V. (DLR)","host_organization_lineage":["https://openalex.org/I2898391981"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"Zeitschriftenbeitrag"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1757796397","https://openalex.org/W2096533821","https://openalex.org/W2121863487","https://openalex.org/W2125069195","https://openalex.org/W2131824593","https://openalex.org/W2164819104","https://openalex.org/W2415731567","https://openalex.org/W2586067474","https://openalex.org/W2733312032","https://openalex.org/W2768629321","https://openalex.org/W2896720518","https://openalex.org/W2963019567","https://openalex.org/W2963809389","https://openalex.org/W2964043796","https://openalex.org/W3003506411","https://openalex.org/W3099664902","https://openalex.org/W3106462682","https://openalex.org/W4298857966","https://openalex.org/W6637967152","https://openalex.org/W6674304311","https://openalex.org/W6679608865","https://openalex.org/W6692846177","https://openalex.org/W6733118196","https://openalex.org/W6746015766"],"related_works":["https://openalex.org/W1496222301","https://openalex.org/W3207760230","https://openalex.org/W1590307681","https://openalex.org/W4312814274","https://openalex.org/W4285370786","https://openalex.org/W2358353312","https://openalex.org/W2353836703","https://openalex.org/W4226458444","https://openalex.org/W3213331859","https://openalex.org/W4226082913"],"abstract_inverted_index":{"State-of-the-art":[0],"multi-robot":[1],"information":[2,17,45],"gathering":[3],"(MR-IG)":[4],"algorithms":[5,28],"often":[6],"rely":[7],"on":[8],"a":[9,54,75],"model":[10],"that":[11,57,78,138,151,164,183],"describes":[12],"the":[13,16,22,44,186],"structure":[14],"of":[15,18,46],"interest":[19],"to":[20,29,35,61,83,86,89,108,156,168],"drive":[21],"robots":[23,85],"motion.":[24],"This":[25,116],"causes":[26],"MR-IG":[27,55,76],"fail":[30],"when":[31],"they":[32],"are":[33,94],"applied":[34,60,155],"new":[36,62],"IG":[37,63,95,114,158,179],"tasks,":[38],"as":[39],"existing":[40,110],"models":[41,100,111],"cannot":[42],"describe":[43],"interest.":[47],"Therefore,":[48,104],"we":[49,72,105,118,125,172],"propose":[50],"in":[51,128,131,146],"this":[52,70],"letter":[53],"algorithm":[56,77,117],"can":[58,153],"be":[59,154],"tasks":[64,96,159],"with":[65,135],"little":[66],"algorithmic":[67,161],"changes.":[68],"To":[69],"end,":[71],"introduce":[73],"DeepIG:":[74],"uses":[79],"deep":[80],"reinforcement":[81],"learning":[82],"allow":[84],"learn":[87],"how":[88],"gather":[90],"information.":[91],"Nevertheless,":[92],"there":[93],"for":[97,112],"which":[98],"accurate":[99],"have":[101],"been":[102],"derived.":[103],"extend":[106],"DeepIG":[107,122,127,152],"exploit":[109],"such":[113],"tasks.":[115],"term":[119],"it":[120,165],"model-based":[121],"(MB-DeepIG).":[123],"First,":[124],"evaluate":[126],"simulations,":[129],"and":[130,163],"an":[132,141],"indoor":[133],"experiment":[134],"three":[136],"quadcopters":[137],"autonomously":[139],"map":[140],"unknown":[142],"terrain":[143],"profile":[144],"built":[145],"our":[147],"lab.":[148],"Results":[149,181],"demonstrate":[150,182],"different":[157],"without":[160],"changes,":[162],"is":[166],"robust":[167],"measurement":[169],"noise.":[170],"Then,":[171],"benchmark":[173],"MB-DeepIG":[174,184],"against":[175],"state-of-theart":[176],"information-driven":[177],"Gaussian-processes-based":[178],"algorithms.":[180],"outperforms":[185],"considered":[187],"benchmarks.":[188]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2025-10-10T00:00:00"}
