Motion planning algorithms and implementations for obstacle-cluttered environments

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Mechanical Engineering

Grantor

University of Illinois Urbana-Champaign

Year dc:date

2025

Author dc:creator

• Tao, Chuyuan

Contributors dc:contributor

• Hovakimyan, Naira
• Stipanovic, Dusan M
• Belabbas, Mohamed Ali
• Yim, Justin

• Optimal Control
• Motion Planning
• Robotics

Statement dc:rights

• Copyright 2025 Chuyuan Tao

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/129415

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F129415