Abstract Two participants with competing objectives are considered in one-to-one orbital pursuit-evasion problem, one of which attempts to satisfy some certain terminal conditions with a minimum cost through its own control while the other player attempts to maximize the specified payoff and avoids entering this terminal range. This paper treats the problem of minimax time to a given final distance, within which the interception between pursuer and evader is defined to occur. To achieve the successful interception with an evader that adopts any unknown maneuvers, a near-optimal interception strategy for the orbital pursuit-evasion is presented in this paper. The pursuit-evasion problem is formulated as a differential game first, and a closed-form barrier solution is provided next. A near-optimal guidance law using deep learning is proposed to intercept the evader inside the capture zone. For the games that start outside the barrier, a learning algorithm for the capture zone embedding strategy is presented based on deep reinforcement learning to help the game state cross the barrier surfaces. The simulation cases of intercepting the evaders that adopt different maneuvering strategies are provided, and the results demonstrate the effectiveness and onboard feasibility of the proposed strategy in solving the orbital interception problem.

    Highlights The closed-form barrier solution for a planar orbital pursuit-evasion game is provided. A deep neural network is trained on a dataset consisting of plenty of barrier trajectories generated through the closed-form barrier formula. The near-optimal guidance law inside capture zone is proposed using trained neural network. A capture zone embedding strategy is presented based on deep reinforcement learning. The closed loop interception strategy is proposed combining the strategies outside and inside capture zone.


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    Titel :

    Near-optimal interception strategy for orbital pursuit-evasion using deep reinforcement learning


    Beteiligte:
    Zhang, Jingrui (Autor:in) / Zhang, Kunpeng (Autor:in) / Zhang, Yao (Autor:in) / Shi, Heng (Autor:in) / Tang, Liang (Autor:in) / Li, Mou (Autor:in)

    Erschienen in:

    Acta Astronautica ; 198 ; 9-25


    Erscheinungsdatum :

    2022-05-31


    Format / Umfang :

    17 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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