Research/ SPACE AI & AUTONOMY

Spacecraft autonomy modeled via Markov decision process and associative rule-based machine learning

Gianni D’Angelo, Massimo Tipaldi, Luigi Glielmo, Salvatore Rampone

IEEE International Workshop on Metrology for AeroSpace (MetroAeroSpace) 2017/Jun 1, 2017/ 22 citations

Space × AI

Technique

Autonomy, Task Planning & OperationsOpenAlex topic: AI-based Problem Solving and Planning · Learning-based MethodsOpenAlex topic: Reinforcement Learning in Robotics

Indexed as

OpenAlexReinforcement Learning in Robotics · AI-based Problem Solving and Planning · Adaptive Dynamic Programming Control

DOI Semantic Scholar OpenAlex

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

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