{"id":"https://openalex.org/W2894417726","doi":"https://doi.org/10.1109/iros.2018.8593772","title":"Deeply Informed Neural Sampling for Robot Motion Planning","display_name":"Deeply Informed Neural Sampling for Robot Motion Planning","publication_year":2018,"publication_date":"2018-10-01","ids":{"openalex":"https://openalex.org/W2894417726","doi":"https://doi.org/10.1109/iros.2018.8593772","mag":"2894417726"},"language":"en","primary_location":{"id":"doi:10.1109/iros.2018.8593772","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros.2018.8593772","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"},"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":"https://openalex.org/A5056336556","display_name":"Ahmed H. Qureshi","orcid":"https://orcid.org/0000-0003-2104-2333"},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ahmed H. Qureshi","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA","institution_ids":["https://openalex.org/I36258959"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054598974","display_name":"Michael C. Yip","orcid":"https://orcid.org/0000-0001-9689-0172"},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Michael C. Yip","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA","institution_ids":["https://openalex.org/I36258959"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I36258959"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":89,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"6582","last_page":"6588"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10586","display_name":"Robotic Path Planning Algorithms","score":1.0,"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":1.0,"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/T10653","display_name":"Robot Manipulation and Learning","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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":0.9965000152587891,"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/motion-planning","display_name":"Motion planning","score":0.7745993733406067},{"id":"https://openalex.org/keywords/workspace","display_name":"Workspace","score":0.7120168805122375},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6877197027206421},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.5311504602432251},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5110920071601868},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5021295547485352},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4551531970500946},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.4453153908252716},{"id":"https://openalex.org/keywords/configuration-space","display_name":"Configuration space","score":0.4325453042984009},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.41792023181915283},{"id":"https://openalex.org/keywords/adaptive-sampling","display_name":"Adaptive sampling","score":0.412390798330307},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.35175392031669617},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3376656174659729},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.2965376675128937},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16232013702392578}],"concepts":[{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.7745993733406067},{"id":"https://openalex.org/C58581272","wikidata":"https://www.wikidata.org/wiki/Q12741163","display_name":"Workspace","level":3,"score":0.7120168805122375},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6877197027206421},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.5311504602432251},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5110920071601868},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5021295547485352},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4551531970500946},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.4453153908252716},{"id":"https://openalex.org/C90738871","wikidata":"https://www.wikidata.org/wiki/Q41642869","display_name":"Configuration space","level":2,"score":0.4325453042984009},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.41792023181915283},{"id":"https://openalex.org/C2781395549","wikidata":"https://www.wikidata.org/wiki/Q4680762","display_name":"Adaptive sampling","level":3,"score":0.412390798330307},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.35175392031669617},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3376656174659729},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2965376675128937},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16232013702392578},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iros.2018.8593772","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros.2018.8593772","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.7099999785423279}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W131069610","https://openalex.org/W1485487406","https://openalex.org/W1485661534","https://openalex.org/W1814533834","https://openalex.org/W1971086298","https://openalex.org/W1971458750","https://openalex.org/W1976930960","https://openalex.org/W2038131922","https://openalex.org/W2086860466","https://openalex.org/W2095705004","https://openalex.org/W2112765698","https://openalex.org/W2143826757","https://openalex.org/W2146502635","https://openalex.org/W2211552581","https://openalex.org/W2218318129","https://openalex.org/W2296385533","https://openalex.org/W2963568027","https://openalex.org/W2967969632","https://openalex.org/W3102121895","https://openalex.org/W3103346840","https://openalex.org/W3106475722","https://openalex.org/W6674330103","https://openalex.org/W6681435938","https://openalex.org/W6688386640"],"related_works":["https://openalex.org/W2005695917","https://openalex.org/W2333571655","https://openalex.org/W985713740","https://openalex.org/W3004930912","https://openalex.org/W2104320275","https://openalex.org/W2111158727","https://openalex.org/W1510039162","https://openalex.org/W30332934","https://openalex.org/W2112736381","https://openalex.org/W2106317420"],"abstract_inverted_index":{"Sampling-based":[0,105],"Motion":[1,106],"Planners":[2],"(SMPs)":[3],"have":[4],"become":[5],"increasingly":[6],"popular":[7],"as":[8],"they":[9,77],"provide":[10],"collision-free":[11,173],"path":[12,64],"solutions":[13],"regardless":[14],"of":[15,31,40,52,72,131,206],"obstacle":[16],"geometry":[17],"in":[18,87,183,214,234,244],"a":[19,49,53,69,95,132,141,147,207],"given":[20,137],"environment.":[21],"However,":[22],"their":[23,83,116],"computational":[24],"complexity":[25],"increases":[26],"significantly":[27,86,119],"with":[28],"the":[29,32,41,156,249],"dimensionality":[30],"motion":[33,101],"planning":[34,102,202,205],"problem.":[35],"Adaptive":[36],"sampling":[37,48],"is":[38,57,177,228],"one":[39],"ways":[42],"to":[43,60,124,170,193],"speed":[44,118],"up":[45],"SMPs":[46,113,169],"by":[47],"particular":[50],"region":[51],"configuration":[54],"space":[55],"that":[56,223],"more":[58],"likely":[59],"contain":[61],"an":[62],"optimal":[63,174],"solution.":[65],"Although":[66],"there":[67],"are":[68],"wide":[70],"variety":[71],"algorithms":[73],"for":[74,100,112,168],"adaptive":[75,98],"sampling,":[76],"rely":[78],"on":[79,200,224],"hand-crafted":[80],"heuristics;":[81],"furthermore,":[82],"performance":[84],"decreases":[85],"high-dimensional":[88],"spaces.":[89],"In":[90],"this":[91],"paper,":[92],"we":[93],"present":[94],"neural":[96,128,152],"network-based":[97],"sampler":[99],"called":[103],"Deep":[104],"Planner":[107],"(DeepSMP).":[108],"DeepSMP":[109,176,199],"generates":[110,165],"samples":[111,167],"and":[114,146,160,163,217,236,239],"enhances":[115],"overall":[117],"while":[120],"exhibiting":[121],"efficient":[122,182],"scalability":[123],"higher-dimensional":[125],"problems.":[126],"DeepSMP's":[127],"architecture":[129],"comprises":[130],"Contractive":[133],"AutoEncoder":[134],"which":[135,154],"encodes":[136],"workspaces":[138],"directly":[139],"from":[140],"raw":[142],"point":[143],"cloud":[144],"data,":[145],"Dropout-based":[148],"stochastic":[149],"deep":[150],"feedforward":[151],"network":[153],"takes":[155],"workspace":[157],"encoding,":[158],"start":[159],"goal":[161],"configuration,":[162],"iteratively":[164],"feasible":[166],"compute":[171],"end-to-end":[172],"paths.":[175],"not":[178],"only":[179],"consistently":[180],"computationally":[181],"all":[184],"tested":[185],"environments":[186],"but":[187],"has":[188],"also":[189],"shown":[190],"remarkable":[191],"generalization":[192],"completely":[194],"unseen":[195],"environments.":[196,219],"We":[197],"evaluate":[198],"multiple":[201],"problems":[203],"including":[204],"point-mass":[208,235],"robot,":[209],"rigid-body,":[210],"6-link":[211,245],"robotic":[212],"manipulator":[213],"various":[215],"2D":[216],"3D":[218],"The":[220],"results":[221],"show":[222],"average":[225],"our":[226],"method":[227],"at":[229],"least":[230],"7":[231],"times":[232,242],"faster":[233,243],"rigid-body":[237],"case":[238,247],"about":[240],"28":[241],"robot":[246],"than":[248],"existing":[250],"state-of-the-art.":[251]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":14},{"year":2024,"cited_by_count":14},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":12},{"year":2021,"cited_by_count":16},{"year":2020,"cited_by_count":14},{"year":2019,"cited_by_count":6}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}