Date of Award

5-2026

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Aerospace, Physics, and Space Sciences

First Advisor

Eric D. Swenson

Second Advisor

Ryan T. White

Third Advisor

Camilo A. Riaño-Rios

Fourth Advisor

Donald Platt

Abstract

The increasing demand for on-orbit servicing (OOS), active debris removal (ADR), and space domain awareness (SDA) missions has increased the need for autonomous spacecraft rendezvous and proximity operations (RPO) with uncooperative and unknown targets. Traditional guidance and control methods are typically designed for cooperative systems with known geometry and state information. This work builds on previous research to develop and evaluate an artificial potential field (APF)-based control framework capable of autonomous operation with minimal prior target knowledge and applicability to both relatively static and tumbling spacecraft.

The proposed APF formulation incorporates established safety constructs from cooperative docking systems, including an approach cone and keep-out sphere, to create a geometry-driven guidance structure. Unlike conventional APF methods, attractive and repulsive field components are defined using generalized spacecraft geometry rather than fixed gains, improving scalability across different targets. Fixed damping terms are incorporated to regulate relative motion and reduce overshoot. The controller is evaluated on six spacecraft models using trajectory analysis and Monte Carlo simulations under relatively static and tumbling conditions, with tumbling rates of 1 deg/s.

Success rates ranged from 97.5% to 100.0% across all tested scenarios, where success is defined as convergence within docking tolerances. Reinforcement learning (RL) was also investigated to improve efficiency by modulating APF activity. Although RL reduced average ΔV consumption, it introduced greater variability and reduced reliability. Results demonstrate that geometry-based APF guidance provides a reliable and generalizable solution for autonomous RPO, while learning-based methods require careful integration for safety-critical applications.

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