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On the Role of AI in Managing Satellite Constellations: Insights from the ConstellAI Project

Created by
  • Haebom

Author

Gregory F. Stock, Juan A. Fraire, Holger Hermanns, J\k{e}drzej Mosi\k{e} zny, Yusra Al-Khazraji, Julio Ram irez Molina, Evridiki V. Ntagiou

Outline

This paper explores how to optimize the operation of large satellite networks using artificial intelligence (AI), based on the European Space Agency (ESA) ConstellAI project. The consortium consisting of GMV GmbH, Saarland University, and Thales Alenia Space developed an AI-based algorithm that proved more efficient than existing methods for two major operational tasks: data routing and resource allocation. Reinforcement learning (RL) was used to improve the end-to-end latency of data routing, and task scheduling was optimized for resource allocation to efficiently use limited resources such as batteries and memory. Experiments on various satellite configurations and operational scenarios demonstrate that RL offers superior flexibility, scalability, and generalization performance compared to existing methods, and is essential for autonomous and intelligent satellite management. The results suggest that AI can provide a more adaptive, robust, and cost-effective satellite network management solution.

Takeaways, Limitations

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Takeaways:
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Experimentally demonstrate that a reinforcement learning (RL)-based AI algorithm outperforms existing methods in data routing and resource allocation problems in satellite networks.
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We demonstrate that AI-enabled satellite network management enables more adaptive, robust, and cost-effective operations.
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We highlight that RL's flexibility, scalability, and generalization performance are essential for autonomous operation of massive satellite networks.
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Limitations:
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Since this study is based on the results of a specific project (ConstellAI), further research is needed to determine generalizability to other satellite systems or operating environments.
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Additional long-term testing and validation in real satellite operating environments is required.
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Lack of detailed analysis of the learning process and performance of RL algorithms.
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A more in-depth evaluation of the efficiency of AI algorithms in terms of energy consumption and computational complexity is needed.
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