This paper proposes an evading strategy based on reinforcement learning for the pursuit-evasion problem considering hypersonic vehicle dynamics (evader) and multiple interceptors (pursuers). Analytical solutions are hard to obtain, so a reinforcement learning method is adopted for the vehicle’s guidance commands in evasion. To cast the problem to a Markov-Decision-Process (MDP), we first establish the motion model involves multiple pursuers and one evader. Moreover, we choose their positions, and velocities change in the three-dimensional space as the state transition, considering a proportional navigation interception guidance. Furthermore, the weighted sum of the zero-effort-miss perpendicular to line-of-sight and the magnitude of maneuver is chosen to be a reward function. After training using a PPO-Clip algorithm, a policy for evasion is obtained. Numerical experiments validate that the method we put forward can be used in the sequential evasion decision against multiple pursuers for a hypersonic vehicle.


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    Titel :

    An evading strategy for hypersonic vehicle against multiple interceptors via reinforcement learning


    Beteiligte:
    Li, Xun (Autor:in) / Zhou, Hongyu (Autor:in)

    Kongress:

    2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022) ; 2022 ; Zhuhai,China


    Erschienen in:

    Proc. SPIE ; 12348


    Erscheinungsdatum :

    10.11.2022





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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