From ion thrusters to solar sails, spacecraft continue to adopt new and more efficient forms of propulsion. As these low-thrust propulsion meth- ods have become more prevalent, new challenges have arisen. Depending on the mission, low-thrust propulsion elements may need to thrust con- tinuously for days/months. During these thrusting periods, external fac- tors, such as a micro-meteoroid impact or a software glitch, may cause the spacecraft to prematurely cease its thrust stage. Half of all deep space missions enter a safe mode where they cannot thrust every four months. These missed thrust events can result in the complete loss of a space- craft for time-dependent trajectories like planetary rendezvous. This paper demonstrates how neural networks, trained using reinforcement learning, can autonomously correct for missed thrust events during an interplanetary trajectory.


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    Titel :

    Using Reinforcement Learning to Design Missed Thrust Resilient Trajectories


    Beteiligte:
    Laipert, Frank E. (Autor:in) / Sood, Rohan (Autor:in) / Bryan, Kyra (Autor:in) / Rubinsztejn, Ari (Autor:in)

    Erscheinungsdatum :

    2020-08-09


    Medientyp :

    Preprint


    Format :

    Keine Angabe


    Sprache :

    Englisch



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