2026-09-20T23:56:34Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/129415 2025-10-20 col_2142_5131 col_2142_14787 com_2142_5130 com_2142_9630 com_2142_234

application/pdf en text 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 Motion planning algorithms and implementations for obstacle-cluttered environments Tao, Chuyuan 2025-04-21 Hovakimyan, Naira Hovakimyan, Naira Stipanovic, Dusan M Belabbas, Mohamed Ali Yim, Justin Optimal Control Motion Planning Robotics eng 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. 2025-05 Thesis https://hdl.handle.net/2142/129415 Copyright 2025 Chuyuan Tao Mechanical Sci & Engineering Mechanical Engineering University of Illinois Urbana-Champaign Ph.D. Dissertation