Spacecraft on-board autonomy is an important topic in currently developed and future space missions. In this study, we present a robust approach to the optimal policy of autonomous space systems modeled via Markov Decision Process (MDP) from the values assigned to its transition probability matrix. After addressing the curse of dimensionality in solving the formulated MDP problem via Approximate Dynamic Programming, we use an Apriori-based Association Classifier to infer a specific optimal policy. Finally, we also assess the effectiveness of such optimal policy in fulfilling the spacecraft autonomy requirements.

Spacecraft Autonomy modeled via Markov Decision Process and Associative Rule-based Machine Learning

D'Angelo, G
;
Glielmo, L;
2017-01-01

Abstract

Spacecraft on-board autonomy is an important topic in currently developed and future space missions. In this study, we present a robust approach to the optimal policy of autonomous space systems modeled via Markov Decision Process (MDP) from the values assigned to its transition probability matrix. After addressing the curse of dimensionality in solving the formulated MDP problem via Approximate Dynamic Programming, we use an Apriori-based Association Classifier to infer a specific optimal policy. Finally, we also assess the effectiveness of such optimal policy in fulfilling the spacecraft autonomy requirements.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4771245
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