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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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


    Contributors:


    Publication date :

    2017-06-01


    Size :

    285268 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English