{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129443"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129443","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Studies in constraint-based search for multi-robot 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":["Lee, Hannah"],"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","Hauser, Kris","Serlin, Zachary","Morales, Marco"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-04-24","date_published":"2025-04-24","updated_at":"2026-07-22T22:25:05Z","subjects":["Artificial Intelligence","Multi-Robot System","Multi-Robot Planning","Search Algorithms","Multi-Agent Pathfinding","Task and Motion Planning"],"languages":["en","eng"],"rights":["Copyright 2025 Hannah Lee"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129443","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Amato, Nancy M","Hauser, Kris","Serlin, Zachary","Morales, Marco"]},{"key":"dc:creator","label":"Author","values":["Lee, Hannah"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-04-24","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":["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":["Artificial Intelligence","Multi-Robot System","Multi-Robot Planning","Search Algorithms","Multi-Agent Pathfinding","Task and Motion 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 Hannah Lee"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129443"]}]},{"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, Hannah Lee, accepted the attached license on 2025-04-24 at 12:44.","The student, Hannah Lee, submitted this Dissertation for approval on 2025-04-24 at 12:53.","This Dissertation was approved for publication on 2025-04-24 at 15:22.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21953 on 2025-10-19 at 18:18:58","Constraint-based search has emerged as a powerful framework for solving multi-agent pathfinding (MAPF) problems by iteratively refining naive solutions through the introduction of constraints. While extensively studied in centralized MAPF, its broader applicability to more complex multi-robot planning problems remains underexplored. This dissertation investigates the adaptability and scalability of constraint-based search across various domains, including large-scale MAPF, decentralized multi-task multi-agent pathfinding (MT-MAPF), and multi-robot task allocation (MRTA). We analyze how constraint selection, search strategies, and distributed computation impact performance, ultimately extending constraint-based search to a diverse range of multi-robot coordination challenges. We begin by introducing a classification system for constraints, offering a structured framework to analyze how different constraint types impact search efficiency and solution quality across various problem representations. Building on this foundation, we address large-scale scalability in MAPF with Hierarchical Composition Conflict-Based Search (HC-CBS), a distributed framework that partitions MAPF problems into smaller, more tractable subproblems. Next, we extend constraint-based search to decentralized Multi-Task Multi-Agent Pathfinding (MT-MAPF) by introducing Pathfinding with Rapid Information Sharing using Motion Constraints (PRISM), which enables agents to plan dynamically in real-time while handling communication constraints. Finally, we integrate constraint-based search with task allocation through Task and Motion Planning Conflict-Based Search (TMP-CBS), a method that jointly optimizes task decomposition, allocation, and motion planning, facilitating structured and efficient multi-robot task execution. Through extensive empirical evaluation, we demonstrate significant improvements in efficiency, scalability, and solution quality across all three domains. Our results show that constraint-based search can be effectively adapted beyond traditional MAPF, facilitating distributed, decentralized, and task-integrated multi-robot planning. This work provides a foundation for further research into scalable, constraint-driven multi-agent coordination methods, with potential applications in warehouse automation, autonomous transportation, and large-scale robotic fleets."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Studies in constraint-based search for multi-robot planning"]}]}],"canonical_facts":{"dc:contributor":["Amato, Nancy M","Hauser, Kris","Serlin, Zachary","Morales, Marco"],"dc:creator":["Lee, Hannah"],"dc:date":["2025-04-24","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, Hannah Lee, accepted the attached license on 2025-04-24 at 12:44.","The student, Hannah Lee, submitted this Dissertation for approval on 2025-04-24 at 12:53.","This Dissertation was approved for publication on 2025-04-24 at 15:22.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21953 on 2025-10-19 at 18:18:58","Constraint-based search has emerged as a powerful framework for solving multi-agent pathfinding (MAPF) problems by iteratively refining naive solutions through the introduction of constraints. While extensively studied in centralized MAPF, its broader applicability to more complex multi-robot planning problems remains underexplored. This dissertation investigates the adaptability and scalability of constraint-based search across various domains, including large-scale MAPF, decentralized multi-task multi-agent pathfinding (MT-MAPF), and multi-robot task allocation (MRTA). We analyze how constraint selection, search strategies, and distributed computation impact performance, ultimately extending constraint-based search to a diverse range of multi-robot coordination challenges. We begin by introducing a classification system for constraints, offering a structured framework to analyze how different constraint types impact search efficiency and solution quality across various problem representations. Building on this foundation, we address large-scale scalability in MAPF with Hierarchical Composition Conflict-Based Search (HC-CBS), a distributed framework that partitions MAPF problems into smaller, more tractable subproblems. Next, we extend constraint-based search to decentralized Multi-Task Multi-Agent Pathfinding (MT-MAPF) by introducing Pathfinding with Rapid Information Sharing using Motion Constraints (PRISM), which enables agents to plan dynamically in real-time while handling communication constraints. Finally, we integrate constraint-based search with task allocation through Task and Motion Planning Conflict-Based Search (TMP-CBS), a method that jointly optimizes task decomposition, allocation, and motion planning, facilitating structured and efficient multi-robot task execution. Through extensive empirical evaluation, we demonstrate significant improvements in efficiency, scalability, and solution quality across all three domains. Our results show that constraint-based search can be effectively adapted beyond traditional MAPF, facilitating distributed, decentralized, and task-integrated multi-robot planning. This work provides a foundation for further research into scalable, constraint-driven multi-agent coordination methods, with potential applications in warehouse automation, autonomous transportation, and large-scale robotic fleets."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129443"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Hannah Lee"],"dc:subject":["Artificial Intelligence","Multi-Robot System","Multi-Robot Planning","Search Algorithms","Multi-Agent Pathfinding","Task and Motion Planning"],"dc:title":["Studies in constraint-based search for multi-robot planning"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:05Z"}