This paper presents a reinforcement-learning (RL)-based robust low-thrust guidance method for asteroid approaching under process uncertainties. Markov decision processes with stochastic dynamics are formulated for RL. To overcome the problem of low terminal accuracy in RL-based transfer trajectory design, robust zero-effort-miss/zero-effort-velocity (R-ZEM/ZEV) guidance is proposed. Originally, an eigenvalue-related term is defined according to the stability conditions of the ZEM/ZEV feedback system and chosen as the learning parameter, which can significantly improve the robustness of the agent to process uncertainties under the low-thrust magnitude constraint. Moreover, the navigation performance of the asteroid approaching is modeled via the Fisher information matrix and incorporated in the reward function design, which enables optimizing the optical observation performance together with the propellant cost. Thereafter, the proximal policy optimization is adopted to train an RL agent that can efficiently deal with the uncertainties. The effectiveness and efficiency of the proposed method are validated through simulations of a low-thrust spacecraft approaching the asteroid Bennu. The promising results indicate that the proposed method can not only deal with various uncertainties efficiently and autonomously but can also guarantee high terminal accuracy.


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    Titel :

    Reinforcement-Learning-Based Robust Guidance for Asteroid Approaching


    Beteiligte:
    Yang, Hongwei (Autor:in) / Hu, Jincheng (Autor:in) / Li, Shuang (Autor:in) / Bai, Xiaoli (Autor:in)


    Erscheinungsdatum :

    2024-07-13


    Format / Umfang :

    15 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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