Conventional UAV multi-type patrol task allocation method mainly uses MEC(mobile edge computing) technology to unload UAV data, which is easily affected by the change of dynamic unloading mechanism, resulting in a high delay in patrol task allocation. Therefore, it is necessary to design a new UAV multi-type patrol task allocation method based on deep reinforcement learning. That is, the deep reinforcement learning technology is used to construct the multi-type inspection task allocation model of UAV, and the multi-type inspection task allocation algorithm of UAV is designed, thus realizing the multi-type inspection task allocation of UAV. The experimental results show that the design of UAV deep reinforcement learning multi-type inspection task allocation method has good distribution effect, reliability and certain application value, and has made certain contributions to improving inspection reliability and reducing comprehensive inspection cost.


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    Title :

    Multi-type patrol task assignment method for UAV based on deep reinforcement learning


    Contributors:

    Conference:

    International Conference on Internet of Things and Machine Learning (IoTML 2023) ; 2023 ; Singapore, Singapore


    Published in:

    Proc. SPIE ; 12937


    Publication date :

    2023-11-29





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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