Close-Proximity Satellite Operations through Deep Reinforcement Learning and Terrestrial Testing Environments
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915176395046912 |
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| author | Lei, Henry Aurand, Joshua Lippay, Zachary S. Phillips, Sean |
| author_facet | Lei, Henry Aurand, Joshua Lippay, Zachary S. Phillips, Sean |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2502_20554 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Close-Proximity Satellite Operations through Deep Reinforcement Learning and Terrestrial Testing Environments Lei, Henry Aurand, Joshua Lippay, Zachary S. Phillips, Sean Robotics Systems and Control 93-05 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. |
| title | Close-Proximity Satellite Operations through Deep Reinforcement Learning and Terrestrial Testing Environments |
| topic | Robotics Systems and Control 93-05 |
| url | https://arxiv.org/abs/2502.20554 |