Date of Award

3-2025

Document Type

Thesis

Degree Name

Master of Science in Computer Science

Department

Department of Electrical and Computer Engineering

First Advisor

Nicholas J. Yielding, PhD

Abstract

The rapidly evolving landscape of space operations necessitates dynamic and autonomous systems to address complex challenges such as Resident Space Object (RSO) inspections. This research explores the application of a Multi-Objective Reinforcement Learning (MORL) framework to rendezvous and proximity operations (RPO), enabling agents to balance conflicting objectives like time efficiency, fuel conservation, and information gain. Unlike traditional reinforcement learning, MORL allows dynamic reweighting of objectives without retraining, offering adaptability and efficiency in multi-objective environments. The study demonstrates MORL's capabilities through custom 2D and 3D simulations of Hill-Clohessy-Wiltshire (HCW) environments and comparing its performance to traditional RL in RPO scenarios. Tasks include navigating to inspection points, optimizing information gain, and balancing time and fuel objectives. This work provides structured approaches for MORL policy development, establishes benchmarks for autonomous RPO tasks, and provides key insights into MORL's potential to enhance autonomy in space operations. The findings validate MORL's effectiveness in optimizing decision-making for complex space missions and lay a foundation for future advancements in autonomous systems, contributing to more sustainable and efficient space exploration.

AFIT Designator

AFIT-ENG-MS-25-M-015

Comments

An embargo was observed for posting this work.

Distribution Statement A: Distribution Unlimited. Approved for public release. PA case number: 88ABW-2025-0213

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