{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129415"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129415","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Motion planning algorithms and implementations for obstacle-cluttered environments","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":["Tao, Chuyuan"],"institution":"University of Illinois Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":["Hovakimyan, Naira","Stipanovic, Dusan M","Belabbas, Mohamed Ali","Yim, Justin"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-04-21","date_published":"2025-04-21","updated_at":"2026-07-22T22:25:05Z","subjects":["Optimal Control","Motion Planning","Robotics"],"languages":["en","eng"],"rights":["Copyright 2025 Chuyuan Tao"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129415","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Hovakimyan, Naira","Stipanovic, Dusan M","Belabbas, Mohamed Ali","Yim, Justin"]},{"key":"dc:creator","label":"Author","values":["Tao, Chuyuan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-04-21","2025-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"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":["Optimal Control","Motion Planning","Robotics"]}]},{"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 Chuyuan Tao"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129415"]}]},{"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, Chuyuan Tao, accepted the attached license on 2025-04-21 at 13:05.","The student, Chuyuan Tao, submitted this Dissertation for approval on 2025-04-21 at 13:11.","This Dissertation was approved for publication on 2025-04-21 at 16:35.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21859 on 2025-10-19 at 18:18:28","Autonomous robotic systems operating in complex and dynamic environments require motion planning algorithms that balance safety, efficiency, and adaptability. Classical path planners, such as A* and Rapidly-exploring Random Trees (RRT), generate geometrically feasible paths but often neglect dynamic constraints and real-time control limitations. Conversely, motion planning algorithms like Model Predictive Control (MPC) and Model Predictive Path Integral (MPPI) control optimize dynamically feasible trajectories but struggle with computational scalability and robustness in cluttered or uncertain environments. This dissertation addresses these challenges through three contributions. First, the RRT-CBF Guided MPPI (RC-MPPI) algorithm enhances sampling-based motion planning by integrating RRT’s global exploration with Control Barrier Functions (CBFs) to filter unsafe trajectories during Monte Carlo sampling, ensuring probabilistic safety in cluttered environments. Second, an optimization-based planning framework leverages B-spline parameterization to generate smooth, continuous-time trajectories that bridge the gap between high-level planning and low-level control execution. Third, a Resilient Estimator-Control Barrier Function (RE-CBF) framework ensures safety at the control level by combining adaptive disturbance observers with safety-critical control, enabling robust operation under unmodeled dynamics and environmental disturbances. Collectively, these contributions enable autonomous systems to navigate dynamic, cluttered environments with formal safety guarantees, computational efficiency, and adaptability to real-world uncertainties."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Motion planning algorithms and implementations for obstacle-cluttered environments"]}]}],"canonical_facts":{"dc:contributor":["Hovakimyan, Naira","Stipanovic, Dusan M","Belabbas, Mohamed Ali","Yim, Justin"],"dc:creator":["Tao, Chuyuan"],"dc:date":["2025-04-21","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, Chuyuan Tao, accepted the attached license on 2025-04-21 at 13:05.","The student, Chuyuan Tao, submitted this Dissertation for approval on 2025-04-21 at 13:11.","This Dissertation was approved for publication on 2025-04-21 at 16:35.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21859 on 2025-10-19 at 18:18:28","Autonomous robotic systems operating in complex and dynamic environments require motion planning algorithms that balance safety, efficiency, and adaptability. Classical path planners, such as A* and Rapidly-exploring Random Trees (RRT), generate geometrically feasible paths but often neglect dynamic constraints and real-time control limitations. Conversely, motion planning algorithms like Model Predictive Control (MPC) and Model Predictive Path Integral (MPPI) control optimize dynamically feasible trajectories but struggle with computational scalability and robustness in cluttered or uncertain environments. This dissertation addresses these challenges through three contributions. First, the RRT-CBF Guided MPPI (RC-MPPI) algorithm enhances sampling-based motion planning by integrating RRT’s global exploration with Control Barrier Functions (CBFs) to filter unsafe trajectories during Monte Carlo sampling, ensuring probabilistic safety in cluttered environments. Second, an optimization-based planning framework leverages B-spline parameterization to generate smooth, continuous-time trajectories that bridge the gap between high-level planning and low-level control execution. Third, a Resilient Estimator-Control Barrier Function (RE-CBF) framework ensures safety at the control level by combining adaptive disturbance observers with safety-critical control, enabling robust operation under unmodeled dynamics and environmental disturbances. Collectively, these contributions enable autonomous systems to navigate dynamic, cluttered environments with formal safety guarantees, computational efficiency, and adaptability to real-world uncertainties."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129415"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Chuyuan Tao"],"dc:subject":["Optimal Control","Motion Planning","Robotics"],"dc:title":["Motion planning algorithms and implementations for obstacle-cluttered environments"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:05Z"}