The autonomous vision-based Unmanned Aerial Vehicles (UAVs) landing is an adaptive way to land in special environments such as the global positioning system denied. There is a risk of collision when multiple UAVs land simultaneously without communication on the same platform. This work accomplishes vision-based autonomous landing and uses a deep-learning-based method to realize collision avoidance during the landing process. Specifically, the landing UAVs are categorized into Level I and II. The YoloV4 deep learning method will be implemented by the Level II UAV to achieve object detection of Level I UAV. Once the Level I UAV’s landing has been detected by the onboard camera of Level II UAV, it will move and land on a relative landing zone beside the Level I UAV. The experiment results show the validity and practicality of our theory.


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    Titel :

    Autonomous Vision-Based UAV Landing with Collision Avoidance Using Deep Learning


    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.


    Beteiligte:
    Arai, Kohei (Herausgeber:in) / Liao, Tianpei (Autor:in) / Haridevan, Amal (Autor:in) / Liu, Yibo (Autor:in) / Shan, Jinjun (Autor:in)

    Kongress:

    Science and Information Conference ; 2022 July 14, 2022 - July 15, 2022


    Erschienen in:

    Intelligent Computing ; Kapitel : 6 ; 79-87


    Erscheinungsdatum :

    07.07.2022


    Format / Umfang :

    9 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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