In systems where unmanned navigation and manned behavior of ships coexist, the intelligence-driven unmanned ships lack the adaptability to complex environments that humans exhibit when faced with realistic navigation environments and will affect the decision judgment of manned ships. To solve the problem, a knowledge distillation-based multi-intelligent body maritime collision avoidance navigation method for human-like behavior is proposed. The method first preprocesses the AIS data, and the core technique is to use the knowledge distillation method to combine the expert strategy with the reinforcement learning method, which introduces the human piloting ship habits and makes the ship’s automatic navigation exhibit human characteristics. The test results show that the unmanned ship trained by the method can learn human driving ship habits outside the reinforcement learning reward function setting, and enhance the training efficiency of reinforcement learning.


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    Titel :

    Personified multi-ship collision avoidance navigation on knowledge distillation


    Beteiligte:
    Freris, Nikolaos M. (Herausgeber:in) / Chen, Lei (Herausgeber:in) / Zhong, Qinyan (Autor:in) / Zhang, Yang (Autor:in)

    Kongress:

    Second International Symposium on Computer Applications and Information Systems (ISCAIS 2023) ; 2023 ; Chengdu, China


    Erschienen in:

    Proc. SPIE ; 12721


    Erscheinungsdatum :

    2023-06-26





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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