{"id":"https://openalex.org/W4313855813","doi":"https://doi.org/10.1109/lra.2023.3235678","title":"Deep-PANTHER: Learning-Based Perception-Aware Trajectory Planner in Dynamic Environments","display_name":"Deep-PANTHER: Learning-Based Perception-Aware Trajectory Planner in Dynamic Environments","publication_year":2023,"publication_date":"2023-01-09","ids":{"openalex":"https://openalex.org/W4313855813","doi":"https://doi.org/10.1109/lra.2023.3235678"},"language":"en","primary_location":{"id":"doi:10.1109/lra.2023.3235678","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lra.2023.3235678","pdf_url":null,"source":{"id":"https://openalex.org/S4210169774","display_name":"IEEE Robotics and Automation Letters","issn_l":"2377-3766","issn":["2377-3766"],"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/A5036406890","display_name":"Jesus Tordesillas","orcid":"https://orcid.org/0000-0001-6848-4070"},"institutions":[{"id":"https://openalex.org/I4210157567","display_name":"Aerospace Testing (United States)","ror":"https://ror.org/04j7z5d67","country_code":"US","type":"company","lineage":["https://openalex.org/I4210157567"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jesus Tordesillas","raw_affiliation_strings":["Aerospace Controls Laboratory, MIT, Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"Aerospace Controls Laboratory, MIT, Cambridge, MA, USA","institution_ids":["https://openalex.org/I4210157567"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011665886","display_name":"Jonathan P. How","orcid":"https://orcid.org/0000-0001-8576-1930"},"institutions":[{"id":"https://openalex.org/I4210157567","display_name":"Aerospace Testing (United States)","ror":"https://ror.org/04j7z5d67","country_code":"US","type":"company","lineage":["https://openalex.org/I4210157567"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jonathan P. How","raw_affiliation_strings":["Aerospace Controls Laboratory, MIT, Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"Aerospace Controls Laboratory, MIT, Cambridge, MA, USA","institution_ids":["https://openalex.org/I4210157567"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5036406890"],"corresponding_institution_ids":["https://openalex.org/I4210157567"],"apc_list":null,"apc_paid":null,"fwci":4.737,"has_fulltext":false,"cited_by_count":39,"citation_normalized_percentile":{"value":0.96179276,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"8","issue":"3","first_page":"1399","last_page":"1406"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10586","display_name":"Robotic Path Planning Algorithms","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10586","display_name":"Robotic Path Planning Algorithms","score":0.9998999834060669,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9977999925613403,"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"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9945999979972839,"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/trajectory","display_name":"Trajectory","score":0.8201326131820679},{"id":"https://openalex.org/keywords/obstacle","display_name":"Obstacle","score":0.7554745674133301},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7023488283157349},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6922402381896973},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6563993692398071},{"id":"https://openalex.org/keywords/trajectory-optimization","display_name":"Trajectory optimization","score":0.5760827660560608},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.45276984572410583},{"id":"https://openalex.org/keywords/obstacle-avoidance","display_name":"Obstacle avoidance","score":0.4267578125},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.36093616485595703},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3385055959224701},{"id":"https://openalex.org/keywords/mobile-robot","display_name":"Mobile robot","score":0.17840361595153809},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07181990146636963}],"concepts":[{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.8201326131820679},{"id":"https://openalex.org/C2776650193","wikidata":"https://www.wikidata.org/wiki/Q264661","display_name":"Obstacle","level":2,"score":0.7554745674133301},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7023488283157349},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6922402381896973},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6563993692398071},{"id":"https://openalex.org/C173246807","wikidata":"https://www.wikidata.org/wiki/Q7833062","display_name":"Trajectory optimization","level":3,"score":0.5760827660560608},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.45276984572410583},{"id":"https://openalex.org/C6683253","wikidata":"https://www.wikidata.org/wiki/Q7075535","display_name":"Obstacle avoidance","level":4,"score":0.4267578125},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.36093616485595703},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3385055959224701},{"id":"https://openalex.org/C19966478","wikidata":"https://www.wikidata.org/wiki/Q4810574","display_name":"Mobile robot","level":3,"score":0.17840361595153809},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07181990146636963},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lra.2023.3235678","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lra.2023.3235678","pdf_url":null,"source":{"id":"https://openalex.org/S4210169774","display_name":"IEEE Robotics and Automation Letters","issn_l":"2377-3766","issn":["2377-3766"],"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"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/14","display_name":"Life below water","score":0.41999998688697815}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W1579853615","https://openalex.org/W1980969546","https://openalex.org/W2222512263","https://openalex.org/W2252355370","https://openalex.org/W2557169239","https://openalex.org/W2557519264","https://openalex.org/W2614122538","https://openalex.org/W2798788395","https://openalex.org/W2883162277","https://openalex.org/W2948479456","https://openalex.org/W2962894046","https://openalex.org/W2963689432","https://openalex.org/W2973216148","https://openalex.org/W2979863294","https://openalex.org/W2990376862","https://openalex.org/W3000103029","https://openalex.org/W3034679090","https://openalex.org/W3038825904","https://openalex.org/W3091367268","https://openalex.org/W3096609285","https://openalex.org/W3104218139","https://openalex.org/W3135496326","https://openalex.org/W3158675508","https://openalex.org/W3159537771","https://openalex.org/W3170672542","https://openalex.org/W3183413118","https://openalex.org/W3201459714","https://openalex.org/W3202883604","https://openalex.org/W4396738453","https://openalex.org/W6631190155","https://openalex.org/W6634817459","https://openalex.org/W6640174482","https://openalex.org/W6681912825"],"related_works":["https://openalex.org/W2930076404","https://openalex.org/W4253519380","https://openalex.org/W2071957557","https://openalex.org/W2596413128","https://openalex.org/W4391249562","https://openalex.org/W2356867392","https://openalex.org/W2782776446","https://openalex.org/W3043170174","https://openalex.org/W2155948905","https://openalex.org/W2357323510"],"abstract_inverted_index":{"This":[0],"letter":[1],"presents":[2],"Deep-PANTHER,":[3],"a":[4,39,59,70,77,99,130],"learning-based":[5],"perception-aware":[6],"trajectory":[7,27,107,114],"planner":[8],"for":[9],"unmanned":[10],"aerial":[11],"vehicles":[12],"(UAVs)":[13],"in":[14,47,115,169],"dynamic":[15,40],"environments.":[16],"Given":[17],"the":[18,22,25,31,48,54,94,116,123,126,139,166],"current":[19],"state":[20],"of":[21,30,50,53,90,125],"UAV,":[23],"and":[24,28,128],"predicted":[26],"size":[29],"obstacle,":[32],"Deep-PANTHER":[33,71,119,153],"generates":[34],"multiple":[35],"trajectories":[36,162],"to":[37,68,110,138,144,157,160],"avoid":[38],"obstacle":[41,161],"while":[42,97],"simultaneously":[43],"maximizing":[44],"its":[45],"presence":[46],"field":[49],"view":[51],"(FOV)":[52],"onboard":[55],"camera.":[56],"To":[57],"obtain":[58],"computationally":[60],"tractable":[61],"real-time":[62],"solution,":[63],"imitation":[64],"learning":[65],"is":[66,108,142,154],"leveraged":[67],"train":[69],"policy":[72],"using":[73],"demonstrations":[74],"provided":[75],"by":[76],"multimodal":[78],"optimization-based":[79,95],"expert.":[80],"Extensive":[81],"simulations":[82],"show":[83],"replanning":[84],"times":[85,146],"that":[86,104,141,163],"are":[87],"two":[88],"orders":[89],"magnitude":[91],"faster":[92],"than":[93,148],"expert,":[96],"achieving":[98],"similar":[100],"cost.":[101],"By":[102],"ensuring":[103],"each":[105],"expert":[106,140],"assigned":[109],"one":[111],"distinct":[112],"student":[113],"loss":[117,135],"function,":[118],"can":[120],"also":[121,155],"capture":[122],"multimodality":[124],"problem":[127],"achieve":[129],"mean":[131],"squared":[132],"error":[133],"(MSE)":[134],"with":[136],"respect":[137],"up":[143],"18":[145],"smaller":[147],"state-of-the-art":[149],"(Relaxed)":[150],"Winner-Takes-All":[151],"approaches.":[152],"shown":[156],"generalize":[158],"well":[159],"differ":[164],"from":[165],"ones":[167],"used":[168],"training.":[170]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":15},{"year":2024,"cited_by_count":14},{"year":2023,"cited_by_count":8}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
