U. Romagnoli
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The increasing traffic in Low Earth Orbit (LEO) necessitates effective and timely Collision Avoidance Manoeuvres (CAMs) planning. However, standard procedures neglect the epistemic uncertainty present in Conjunction Data Messages, thereby compromising the robustness of the resulting manoeuvres. While a method accounting for both epistemic and aleatory uncertainties exists, its high computational cost limits real-world scalability.
This thesis investigates the application of Deep Reinforcement Learning to address this limitation by developing a Deep Contextual Bandit agent for robust CAM planning. The proposed agent generates nearly optimal CAMs accounting for epistemic and aleatory uncertainty in under a second, significantly reducing the runtime compared to traditional numerical approaches. When evaluated on a synthetic database of nearly circular LEO orbits, the agent successfully minimised the worst-case probability of collision in over 90% of the test cases. ...
This thesis investigates the application of Deep Reinforcement Learning to address this limitation by developing a Deep Contextual Bandit agent for robust CAM planning. The proposed agent generates nearly optimal CAMs accounting for epistemic and aleatory uncertainty in under a second, significantly reducing the runtime compared to traditional numerical approaches. When evaluated on a synthetic database of nearly circular LEO orbits, the agent successfully minimised the worst-case probability of collision in over 90% of the test cases. ...
The increasing traffic in Low Earth Orbit (LEO) necessitates effective and timely Collision Avoidance Manoeuvres (CAMs) planning. However, standard procedures neglect the epistemic uncertainty present in Conjunction Data Messages, thereby compromising the robustness of the resulting manoeuvres. While a method accounting for both epistemic and aleatory uncertainties exists, its high computational cost limits real-world scalability.
This thesis investigates the application of Deep Reinforcement Learning to address this limitation by developing a Deep Contextual Bandit agent for robust CAM planning. The proposed agent generates nearly optimal CAMs accounting for epistemic and aleatory uncertainty in under a second, significantly reducing the runtime compared to traditional numerical approaches. When evaluated on a synthetic database of nearly circular LEO orbits, the agent successfully minimised the worst-case probability of collision in over 90% of the test cases.
This thesis investigates the application of Deep Reinforcement Learning to address this limitation by developing a Deep Contextual Bandit agent for robust CAM planning. The proposed agent generates nearly optimal CAMs accounting for epistemic and aleatory uncertainty in under a second, significantly reducing the runtime compared to traditional numerical approaches. When evaluated on a synthetic database of nearly circular LEO orbits, the agent successfully minimised the worst-case probability of collision in over 90% of the test cases.