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.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

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


    Beteiligte:
    Ahmad, Badrul Hisham bin (Herausgeber:in) / Subramaniyam, Kannimuthu (Herausgeber:in) / Lin, Zhiming (Autor:in) / Gui, Huiyang (Autor:in) / Guan, Yuyang (Autor:in) / Xie, Renjie (Autor:in) / Wang, Kun (Autor:in)

    Kongress:

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


    Erschienen in:

    Proc. SPIE ; 12937


    Erscheinungsdatum :

    29.11.2023





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Task Assignment of UAV Swarms Based on Deep Reinforcement Learning

    Bo Liu / Shulei Wang / Qinghua Li et al. | DOAJ | 2023

    Freier Zugriff



    Multi-UAV Cooperative Target Assignment Method Based on Reinforcement Learning

    Yunlong Ding / Minchi Kuang / Heng Shi et al. | DOAJ | 2024

    Freier Zugriff