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arXiv · 2502.20554

Close-Proximity Satellite Operations through Deep Reinforcement Learning and Terrestrial Testing Environments

Henry Lei·Joshua Aurand·Zachary S. Lippay·Sean Phillips
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Abstract

With the increasingly congested and contested space environment, safe and effective satellite operation has become increasingly challenging. As a result, there is growing interest in autonomous satellite capabilities, with common machine learning techniques gaining attention for their potential to address complex decision-making in the space domain. However, the "black-box" nature of many of these methods results in difficulty understanding the model's input/output relationship and more specifically its sensitivity to environmental disturbances, sensor noise, and control intervention. This paper explores the use of Deep Reinforcement Learning (DRL) for satellite control in multi-agent inspection tasks. The Local Intelligent Network of Collaborative Satellites (LINCS) Lab is used to test the performance of these control algorithms across different environments, from simulations to real-world quadrotor UAV hardware, with a particular focus on understanding their behavior and potential degradation in performance when deployed beyond the training environment.

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Henry Lei, Joshua Aurand, Zachary S. Lippay, Sean Phillips. 2025-02-27. Close-Proximity Satellite Operations through Deep Reinforcement Learning and Terrestrial Testing Environments. https://arxiv.org/abs/2502.20554

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