{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129848"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129848","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"A framework for guided motion planning","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms","abstract_has_math":false,"creators":["Attali, Amnon David"],"institution":"University of Illinois Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Amato, Nancy M.","Amato, Nancy M","LaValle, Steven M","Morales, Marco","Kavraki, Lydia E."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-07-16","date_published":"2025-07-16","updated_at":"2026-07-22T22:25:06Z","subjects":["Sampling Based Motion Planning","Guided Motion Planning","Robotics","Heuristics","Experience-based Planning"],"languages":["en","eng"],"rights":["Copyright 2025 Amnon Attali"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129848","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Amato, Nancy M.","Amato, Nancy M","LaValle, Steven M","Morales, Marco","Kavraki, Lydia E."]},{"key":"dc:creator","label":"Author","values":["Attali, Amnon David"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-07-16","2025-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"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":["Sampling Based Motion Planning","Guided Motion Planning","Robotics","Heuristics","Experience-based Planning"]}]},{"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 Amnon Attali"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129848"]}]},{"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 2025-10-20 without embargo terms","The student, Amnon Attali, accepted the attached license on 2025-07-09 at 09:35.","The student, Amnon Attali, submitted this Dissertation for approval on 2025-07-11 at 09:52.","This Dissertation was approved for publication on 2025-07-16 at 15:05.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22454 on 2025-10-20 at 16:57:33","The Robotics search problem is computationally difficult, motivating practical approaches that sacrifice generality in favor of effective solutions in realistic scenarios. In this work we aim to unify how one branch of Robotics algorithms, namely the family of Sampling-Based Motion Planning methods, exploit heuristics to do guided search. In order to unify how different methods guide search we start with a simple observation - the Motion Planning problem definition is insufficient for answering questions regarding where guidance comes from, when is it effective, and how is it used. Thus our framework for Guided Motion Planning (GMP) involves a modified problem definition that implies guidance comes from prior experience which is distilled into a data structure we call the Guiding Space. We then propose a simple Guided Search algorithm that uses a Guiding Space, and the heuristics it provides, to do motion planning. By making experience a part of the problem definition we make aspects of motion planning that are traditionally done informally, such as algorithm selection or heuristic design, an explicit component of guided planning. Most of the work we present can be viewed as justifying the proposed framework. To demonstrate generality we show how otherwise incomparable methods in the literature can be brought closer by framing them with our language, including a wide variety of methods that seem to have nothing to do with heuristics. To demonstrate applicability we show how implementing existing ideas from the literature for planning from experience within our framework leads to improved algorithms. Finally we propose metrics for evaluating and learning guidance, showing how this language of learning heuristics from experience is useful for standardizing the evaluation and design of new algorithms, that a simple re-framing can highlight properties of existing algorithms that are otherwise obscured when computing holistic performance."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["A framework for guided motion planning"]}]}],"canonical_facts":{"dc:contributor":["Amato, Nancy M.","Amato, Nancy M","LaValle, Steven M","Morales, Marco","Kavraki, Lydia E."],"dc:creator":["Attali, Amnon David"],"dc:date":["2025-07-16","2025-08"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms","The student, Amnon Attali, accepted the attached license on 2025-07-09 at 09:35.","The student, Amnon Attali, submitted this Dissertation for approval on 2025-07-11 at 09:52.","This Dissertation was approved for publication on 2025-07-16 at 15:05.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22454 on 2025-10-20 at 16:57:33","The Robotics search problem is computationally difficult, motivating practical approaches that sacrifice generality in favor of effective solutions in realistic scenarios. In this work we aim to unify how one branch of Robotics algorithms, namely the family of Sampling-Based Motion Planning methods, exploit heuristics to do guided search. 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To demonstrate generality we show how otherwise incomparable methods in the literature can be brought closer by framing them with our language, including a wide variety of methods that seem to have nothing to do with heuristics. To demonstrate applicability we show how implementing existing ideas from the literature for planning from experience within our framework leads to improved algorithms. Finally we propose metrics for evaluating and learning guidance, showing how this language of learning heuristics from experience is useful for standardizing the evaluation and design of new algorithms, that a simple re-framing can highlight properties of existing algorithms that are otherwise obscured when computing holistic performance."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129848"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Amnon Attali"],"dc:subject":["Sampling Based Motion Planning","Guided Motion Planning","Robotics","Heuristics","Experience-based Planning"],"dc:title":["A framework for guided motion planning"],"dc:type":["text"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:06Z"}