Autonomy is a highly sought-after feature for future civil, commercial, and military Earth-observation space missions. Deep Reinforcement Learning (DRL) techniques have demonstrated state-of-the-art performance for on-line decision making in complex domains such as competitive real-time strat-egy games and black-box control. DRL allows for the direct uti-lization of high-fidelity whole-system simulators without inter-mediate approximations or representations. As a result, agents trained with DRL can explore and exploit subtle dynamics that may be lost when applying approximation-driven techniques for tasking and planning, thereby enabling greater performance. This work describes efforts at Ball Aerospace in developing a DRL-driven solution to the single-satellite, arbitrary-target, single-ground-station planning problem. A design reference mission for this problem, based on the Compact Infrared Ra-diometer in Space (CIRiS) cubesat, is described alongside an im-plementation of this mission using the Basilisk simulation frame-work. The resulting simulator is used as a training environment for the DRL-based agent. This simulator is also used for performance evaluation. The DRL-based agent's performance is compared against the results of a rule-based agent. The results of these approaches are compared using multiple figures of merit, including objective performance, decision-making time, and mission-level resource utilization. The resulting comparison demonstrates the relative merits of DRL as an approach versus heuristic, rule-based command and control architectures.


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    Titel :

    Autonomous Command and Control for Earth-Observing Satellites using Deep Reinforcement Learning


    Beteiligte:
    Harris, Andrew (Autor:in) / Naik, Kedar (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    04.03.2023


    Format / Umfang :

    2854231 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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