{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/122055"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/122055","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Motion pattern prediction in dynamic environments","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms","abstract_has_math":false,"creators":["Arias, Felipe Felix"],"institution":"University of Illinois at 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":2023,"date_issued":"2023-12","date_published":"2023-12","updated_at":"2026-07-22T22:25:00Z","subjects":["Robotics","Navigation","Motion Planning","Dynamic Environments","Multi-agent Systems","Machine Learning"],"languages":["en","eng"],"rights":["Copyright 2023 Felipe Arias"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/122055","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":["Arias, Felipe Felix"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-12","2023-12-05"]},{"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":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Robotics","Navigation","Motion Planning","Dynamic Environments","Multi-agent Systems","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 2023 Felipe Arias"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/122055"]}]},{"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 2024-03-01 without embargo terms","The student, Felipe Arias, accepted the attached license on 2023-12-04 at 14:35.","The student, Felipe Arias, submitted this Thesis for approval on 2023-12-04 at 14:46.","This Thesis was approved for publication on 2023-12-05 at 09:53.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20129 on 2024-03-01 at 13:15:24","Traditionally, motion planning algorithms have tackled the challenge of navigation in dynamic environments by approximating a robot's configuration space through a graph representation. This involves predicting or computing the trajectories of obstacles and finding feasible paths via a pathfinding algorithm. In our work, we strive to enhance the performance of these subproblems by learning to identify regions critical to dynamic environment navigation. We present a novel methodology for constructing sparse probabilistic roadmaps, a two stage approach that combines a self-supervised learning method for recognizing the topology and geometries indicative of motion patterns in dynamic settings, and a sampling-based strategy for leveraging these learned features. The result is the creation of neural networks capable of predicting the probability of occupancy of a given region and Avoidance Critical Probabilistic Roadmaps (ACPRMs), which leverage these insights to significantly improve navigation performance. ACPRMs have shown remarkable performance, demonstrating up to five orders of magnitude improvement over grid-sampling in multi-agent scenarios and surpassing competitive baselines by up to ten orders of magnitude in multi-query situations."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Motion pattern prediction in dynamic environments"]}]}],"canonical_facts":{"dc:contributor":["Amato, Nancy M"],"dc:creator":["Arias, Felipe Felix"],"dc:date":["2023-12","2023-12-05"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms","The student, Felipe Arias, accepted the attached license on 2023-12-04 at 14:35.","The student, Felipe Arias, submitted this Thesis for approval on 2023-12-04 at 14:46.","This Thesis was approved for publication on 2023-12-05 at 09:53.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20129 on 2024-03-01 at 13:15:24","Traditionally, motion planning algorithms have tackled the challenge of navigation in dynamic environments by approximating a robot's configuration space through a graph representation. This involves predicting or computing the trajectories of obstacles and finding feasible paths via a pathfinding algorithm. In our work, we strive to enhance the performance of these subproblems by learning to identify regions critical to dynamic environment navigation. We present a novel methodology for constructing sparse probabilistic roadmaps, a two stage approach that combines a self-supervised learning method for recognizing the topology and geometries indicative of motion patterns in dynamic settings, and a sampling-based strategy for leveraging these learned features. The result is the creation of neural networks capable of predicting the probability of occupancy of a given region and Avoidance Critical Probabilistic Roadmaps (ACPRMs), which leverage these insights to significantly improve navigation performance. ACPRMs have shown remarkable performance, demonstrating up to five orders of magnitude improvement over grid-sampling in multi-agent scenarios and surpassing competitive baselines by up to ten orders of magnitude in multi-query situations."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/122055"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Felipe Arias"],"dc:subject":["Robotics","Navigation","Motion Planning","Dynamic Environments","Multi-agent Systems","Machine Learning"],"dc:title":["Motion pattern prediction in dynamic environments"],"dc:type":["text"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:00Z"}