Multi-Reward Proximal Policy Optimization, a multi-objective deep reinforcement learning algorithm, is used to examine the design space of low-thrust trajectories for a SmallSat transferring between two libration point orbits in the Earth- Moon system. Using Multi-Reward Proximal Policy Optimiza- tion, multiple policies are simultaneously and efficiently trained on three distinct trajectory design scenarios. Each policy is trained to create a unique control scheme based on the trajectory design scenario and assigned reward function: a unique combination of weights scaling competing objectives that guide the spacecraft to the target mission orbit, incentivize faster flight times, and penalize propellant mass usage. Then, the policies are evaluated on the same set of perturbed initial conditions in each scenario to generate the propellant mass usages, flight times, and state discontinuities from a reference trajectory for each control scheme. This solution space of low-thrust trajectories for a SmallSat is used to examine the multi-objective trade space for the trajectory design scenario. By autonomously constructing the solution space, insights into the required propellant mass, flight time, and transfer geometry are rapidly achieved.


    Zugriff

    Zugriff über TIB

    Verfügbarkeit in meiner Bibliothek prüfen


    Exportieren, teilen und zitieren



    Titel :

    Exploring Transfers Between Earth-Moon Halo Orbits via Multi-Objective Reinforcement Learning


    Beteiligte:

    Erscheinungsdatum :

    2021-03-06


    Medientyp :

    Preprint


    Format :

    Keine Angabe


    Sprache :

    Englisch



    Exploring Transfers Between Earth-Moon Halo Orbits via Multi-Objective Reinforcement Learning

    C. J. Sullivan / R. L. Anderson / N. Bosanac et al. | NTIS | 2021


    Exploring Transfers Between Earth-Moon Halo Orbits via Multi-Objective Reinforcement Learning

    Christopher J Sullivan / Rodney L Anderson / Natasha Bosanac et al. | NTRS


    Exploring Transfers between Earth-Moon Halo Orbits via Multi-Objective Reinforcement Learning

    Sullivan, Christopher J. / Bosanac, Natasha / Anderson, Rodney L. et al. | IEEE | 2021


    Exploring Transfers Between Earth-Moon Halo Orbits via Multi-Objective Reinforcement Learning

    Stuart, Jeffrey R. / Mashiku, Alinda K. / Anderson, Rodney L et al. | NTRS | 2021


    Transfers to Earth–Moon L3 halo orbits

    Davis, Kathryn | Online Contents | 2013