Summary of Neural-based Control For Cubesat Docking Maneuvers, by Matteo Stoisa et al.
Neural-based Control for CubeSat Docking Maneuvers
by Matteo Stoisa, Federica Paganelli Azza, Luca Romanelli, Mattia Varile
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed approach employs Artificial Neural Networks (ANN) trained through Reinforcement Learning (RL) for autonomous spacecraft guidance and control during the final phase of the rendezvous maneuver. This innovative strategy is easily implementable onboard and offers fast adaptability and robustness to disturbances by learning control policies from experience rather than relying on predefined models. The proposed approach is validated through extensive Monte Carlo simulations within a relevant environment in 6DoF settings, as well as hardware tests that demonstrate deployment feasibility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Autonomous spacecraft can rendezvous and dock with other vehicles or stations without human intervention. This process requires precise guidance and control to ensure safe and efficient docking. Researchers have developed an innovative approach using artificial intelligence (AI) to improve the accuracy and efficiency of this process. The AI system learns through experience, allowing it to adapt quickly to changing conditions and unexpected disturbances. Tests showed that this approach can be implemented onboard spacecraft and is effective in real-world scenarios. |
Keywords
* Artificial intelligence * Reinforcement learning
