Motion planning algorithms and implementations for obstacle-cluttered environments

Tao, Chuyuan

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

Description

Title

Motion planning algorithms and implementations for obstacle-cluttered environments

Author(s)

Tao, Chuyuan

Issue Date

2025-04-21

Director of Research (if dissertation) or Advisor (if thesis)

Hovakimyan, Naira

Doctoral Committee Chair(s)

Hovakimyan, Naira

Committee Member(s)

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

Department of Study

Mechanical Sci & Engineering

Discipline

Mechanical Engineering

Degree Granting Institution

University of Illinois Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

Optimal Control, Motion Planning, Robotics

Abstract

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.

Graduation Semester

2025-05

Type of Resource

Thesis

Handle URL

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

Copyright and License Information

Copyright 2025 Chuyuan Tao

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