{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129200"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129200","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Database filtering strategies for experience-based motion planning","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","abstract_has_math":false,"creators":["Telagi, Praval"],"institution":"University of Illinois Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Amato, Nancy M"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-04-15","date_published":"2025-04-15","updated_at":"2026-07-22T22:25:04Z","subjects":["Motion Planning","Machine Learning"],"languages":["en","eng"],"rights":["Copyright 2025 Praval Telagi"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129200","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Amato, Nancy M"]},{"key":"dc:creator","label":"Author","values":["Telagi, Praval"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-04-15","2025-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Motion Planning","Machine Learning"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2025 Praval Telagi"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129200"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","The student, Praval Telagi, accepted the attached license on 2025-04-14 at 15:35.","The student, Praval Telagi, submitted this Thesis for approval on 2025-04-14 at 15:39.","This Thesis was approved for publication on 2025-04-15 at 05:51.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21770 on 2025-10-19 at 18:09:23","Learning from previous experience in motion planning has been shown to significantly reduce search time in new, unseen motion planning problems. Often, these experiences are stored in a database as paths and a new problem will query the database for the most relevant solutions. Prior work in experience based motion planning tends to use limited information, such as only task information, about the motion planning problem when querying a database for solutions. Additionally, many prior methods fail to provide a principled way of generating a good experience database that allows their algorithm to perform optimally. We study the effects of a variety of filtering methods on a database for a novel motion planning algorithm Path Database Guidance (PDG) to understand which method consistently minimizes the average number of collision checks performed during search. Specifically, we analyze augmenting the database by performing random filtering, cluster-based filtering, and model-based filtering. Our results illustrate the significant impact that databases can have on the performance of PDG in solving various motion planning problems. Lastly, we show our model-based filtering method consistently minimizes the average number of collision checks across problem domains using information about the environment."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Database filtering strategies for experience-based motion planning"]}]}],"canonical_facts":{"dc:contributor":["Amato, Nancy M"],"dc:creator":["Telagi, Praval"],"dc:date":["2025-04-15","2025-05"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","The student, Praval Telagi, accepted the attached license on 2025-04-14 at 15:35.","The student, Praval Telagi, submitted this Thesis for approval on 2025-04-14 at 15:39.","This Thesis was approved for publication on 2025-04-15 at 05:51.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21770 on 2025-10-19 at 18:09:23","Learning from previous experience in motion planning has been shown to significantly reduce search time in new, unseen motion planning problems. Often, these experiences are stored in a database as paths and a new problem will query the database for the most relevant solutions. Prior work in experience based motion planning tends to use limited information, such as only task information, about the motion planning problem when querying a database for solutions. Additionally, many prior methods fail to provide a principled way of generating a good experience database that allows their algorithm to perform optimally. We study the effects of a variety of filtering methods on a database for a novel motion planning algorithm Path Database Guidance (PDG) to understand which method consistently minimizes the average number of collision checks performed during search. Specifically, we analyze augmenting the database by performing random filtering, cluster-based filtering, and model-based filtering. Our results illustrate the significant impact that databases can have on the performance of PDG in solving various motion planning problems. Lastly, we show our model-based filtering method consistently minimizes the average number of collision checks across problem domains using information about the environment."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129200"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Praval Telagi"],"dc:subject":["Motion Planning","Machine Learning"],"dc:title":["Database filtering strategies for experience-based motion planning"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:04Z"}