{"id":"https://openalex.org/W3003936704","doi":"https://doi.org/10.1109/iros40897.2019.8968147","title":"Maximum Information Bounds for Planning Active Sensing Trajectories","display_name":"Maximum Information Bounds for Planning Active Sensing Trajectories","publication_year":2019,"publication_date":"2019-11-01","ids":{"openalex":"https://openalex.org/W3003936704","doi":"https://doi.org/10.1109/iros40897.2019.8968147","mag":"3003936704"},"language":"en","primary_location":{"id":"doi:10.1109/iros40897.2019.8968147","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros40897.2019.8968147","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-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/A5057774600","display_name":"Brent Schlotfeldt","orcid":"https://orcid.org/0000-0002-2016-6492"},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Brent Schlotfeldt","raw_affiliation_strings":["University of Pennsylvania,GRASP Laboratory,USA","GRASP Laboratory, University of Pennsylvania, USA"],"affiliations":[{"raw_affiliation_string":"University of Pennsylvania,GRASP Laboratory,USA","institution_ids":["https://openalex.org/I79576946"]},{"raw_affiliation_string":"GRASP Laboratory, University of Pennsylvania, USA","institution_ids":["https://openalex.org/I79576946"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066400889","display_name":"Nikolay Atanasov","orcid":"https://orcid.org/0000-0003-0272-7580"},"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"]},{"id":"https://openalex.org/I2800935791","display_name":"UC San Diego Health System","ror":"https://ror.org/01kbfgm16","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I2800935791"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nikolay Atanasov","raw_affiliation_strings":["UC San Diego,Electrical and Computer Engineering department,USA","Electrical and Computer Engineering department, UC San Diego, USA"],"affiliations":[{"raw_affiliation_string":"UC San Diego,Electrical and Computer Engineering department,USA","institution_ids":["https://openalex.org/I2800935791"]},{"raw_affiliation_string":"Electrical and Computer Engineering department, UC San Diego, USA","institution_ids":["https://openalex.org/I36258959"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029243115","display_name":"George J. Pappas","orcid":"https://orcid.org/0000-0001-9081-0637"},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"George J. Pappas","raw_affiliation_strings":["University of Pennsylvania,GRASP Laboratory,USA","GRASP Laboratory, University of Pennsylvania, USA"],"affiliations":[{"raw_affiliation_string":"University of Pennsylvania,GRASP Laboratory,USA","institution_ids":["https://openalex.org/I79576946"]},{"raw_affiliation_string":"GRASP Laboratory, University of Pennsylvania, USA","institution_ids":["https://openalex.org/I79576946"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5057774600"],"corresponding_institution_ids":["https://openalex.org/I79576946"],"apc_list":null,"apc_paid":null,"fwci":0.9801,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.82872738,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"4913","last_page":"4920"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10906","display_name":"AI-based Problem Solving and Planning","score":0.9950000047683716,"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/T10906","display_name":"AI-based Problem Solving and Planning","score":0.9950000047683716,"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":0.992900013923645,"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/T11010","display_name":"Logic, Reasoning, and Knowledge","score":0.9905999898910522,"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/computer-science","display_name":"Computer science","score":0.6706221699714661},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.6090796589851379},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.5814815759658813},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.5643618106842041},{"id":"https://openalex.org/keywords/motion-planning","display_name":"Motion planning","score":0.5309664011001587},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5252218246459961},{"id":"https://openalex.org/keywords/bellman-equation","display_name":"Bellman equation","score":0.5161577463150024},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.489610493183136},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.47345346212387085},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4726758599281311},{"id":"https://openalex.org/keywords/heuristics","display_name":"Heuristics","score":0.4629673957824707},{"id":"https://openalex.org/keywords/markov-decision-process","display_name":"Markov decision process","score":0.44057950377464294},{"id":"https://openalex.org/keywords/dijkstras-algorithm","display_name":"Dijkstra's algorithm","score":0.41823461651802063},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4151459038257599},{"id":"https://openalex.org/keywords/monte-carlo-tree-search","display_name":"Monte Carlo tree search","score":0.4131714403629303},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.3499413728713989},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.34800827503204346},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3092982769012451},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.24050799012184143},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.21735617518424988},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1898462176322937},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.12437379360198975},{"id":"https://openalex.org/keywords/shortest-path-problem","display_name":"Shortest path problem","score":0.11401602625846863}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6706221699714661},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.6090796589851379},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.5814815759658813},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5643618106842041},{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.5309664011001587},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5252218246459961},{"id":"https://openalex.org/C14646407","wikidata":"https://www.wikidata.org/wiki/Q1430750","display_name":"Bellman equation","level":2,"score":0.5161577463150024},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.489610493183136},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.47345346212387085},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4726758599281311},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.4629673957824707},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.44057950377464294},{"id":"https://openalex.org/C173870130","wikidata":"https://www.wikidata.org/wiki/Q8548","display_name":"Dijkstra's algorithm","level":4,"score":0.41823461651802063},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4151459038257599},{"id":"https://openalex.org/C46149586","wikidata":"https://www.wikidata.org/wiki/Q11785332","display_name":"Monte Carlo tree search","level":3,"score":0.4131714403629303},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.3499413728713989},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.34800827503204346},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3092982769012451},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.24050799012184143},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.21735617518424988},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1898462176322937},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.12437379360198975},{"id":"https://openalex.org/C22590252","wikidata":"https://www.wikidata.org/wiki/Q1058754","display_name":"Shortest path problem","level":3,"score":0.11401602625846863},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"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/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iros40897.2019.8968147","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros40897.2019.8968147","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4099999964237213,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W198496542","https://openalex.org/W200217546","https://openalex.org/W1482849335","https://openalex.org/W1489154522","https://openalex.org/W1562003885","https://openalex.org/W1969483458","https://openalex.org/W1972624884","https://openalex.org/W2023716923","https://openalex.org/W2045333994","https://openalex.org/W2072931689","https://openalex.org/W2114144879","https://openalex.org/W2114549813","https://openalex.org/W2143088799","https://openalex.org/W2145729159","https://openalex.org/W2160540358","https://openalex.org/W2161076907","https://openalex.org/W2164429173","https://openalex.org/W2322076341","https://openalex.org/W2336416123","https://openalex.org/W2461937780","https://openalex.org/W2520459424","https://openalex.org/W2736267156","https://openalex.org/W2790514337","https://openalex.org/W2803699674","https://openalex.org/W2964162338","https://openalex.org/W3124420883","https://openalex.org/W6608136372","https://openalex.org/W6683620479"],"related_works":["https://openalex.org/W2152670157","https://openalex.org/W2386410636","https://openalex.org/W176737593","https://openalex.org/W2808418668","https://openalex.org/W2903299703","https://openalex.org/W2016648086","https://openalex.org/W3105579180","https://openalex.org/W2156021013","https://openalex.org/W4399157305","https://openalex.org/W4281791088"],"abstract_inverted_index":{"This":[0],"paper":[1],"considers":[2],"the":[3,23,43,63,84,95,134,160,167],"problem":[4,48,66,99,104],"of":[5,133],"planning":[6,103,121],"trajectories":[7],"for":[8],"robots":[9],"equipped":[10],"with":[11,156],"sensors":[12],"whose":[13],"task":[14],"is":[15,39,54],"to":[16,41,49,57],"track":[17],"an":[18,154],"evolving":[19],"target":[20],"process":[21],"in":[22,83,119,141,153,162],"world.":[24],"We":[25,158],"focus":[26],"on":[27,61,68,82],"processes":[28],"which":[29,38,53,137],"can":[30,111,138,172],"be":[31,139],"represented":[32],"by":[33,73],"a":[34,50,70,87,101,125,131],"Gaussian":[35],"random":[36],"variable,":[37],"known":[40],"reduce":[42],"general":[44],"stochastic":[45],"information":[46,142],"acquisition":[47,143],"deterministic":[51,65,102],"problem,":[52],"much":[55],"simpler":[56],"solve.":[58],"Previous":[59],"work":[60,92],"solving":[62],"resulting":[64,168],"focuses":[67],"computing":[69],"search":[71,85],"tree":[72],"Forward":[74],"Value":[75],"Iteration":[76],"and":[77,109,127,150],"pruning":[78],"uninformative":[79],"nodes":[80],"early":[81],"via":[86],"domination":[88],"criteria.":[89],"In":[90],"this":[91],"we":[93,123],"formulate":[94],"Active":[96],"Information":[97],"Acquisition":[98],"as":[100,130,146],"where":[105],"algorithms":[106],"like":[107],"Dijkstra":[108],"A*":[110,117],"produce":[112],"optimal":[113,174],"solutions.":[114],"To":[115],"use":[116],"effectively":[118],"long":[120],"horizons":[122],"derive":[124],"consistent":[126],"admissible":[128],"heuristic":[129,169],"function":[132],"sensor":[135],"model":[136],"used":[140],"tasks":[144],"such":[145],"actively":[147],"mapping":[148],"static":[149],"moving":[151],"targets":[152],"environment":[155],"obstacles.":[157],"validate":[159],"results":[161],"several":[163],"simulations":[164],"indicating":[165],"that":[166],"informed":[170],"algorithm":[171],"recover":[173],"solutions":[175],"faster":[176],"than":[177],"existing":[178],"search-based":[179],"methods.":[180]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}