The use of unmanned aerial vehicles (UAVs) or drones have become ubiquitous in the recent years. Collision avoidance is a critical component of path planning, allowing multi-agent networks of cooperative UAVs to work together towards common objectives while avoiding each other. We implemented, integrated and evaluated the effectiveness of using a low cost, wide angle monocular camera with real-time computer vision algorithms to detect and track other UAVs in local airspace and perform collision avoidance in the event of a communications degradation or the presence of non-cooperative adversaries, through experimental flight tests where the UAVs were set on collision courses.


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    Titel :

    Vision-Based Sense and Avoid with Monocular Vision and Real-Time Object Detection for UAVs


    Beteiligte:
    Leong, Wai Lun (Autor:in) / Wang, Pengfei (Autor:in) / Huang, Sunan (Autor:in) / Ma, Zhengtian (Autor:in) / Yang, Hong (Autor:in) / Sun, Jingxuan (Autor:in) / Zhou, Yu (Autor:in) / Abdul Hamid, Mohamed Redhwan (Autor:in) / Srigrarom, Sutthiphong (Autor:in) / Teo, Rodney (Autor:in)


    Erscheinungsdatum :

    15.06.2021


    Format / Umfang :

    3202906 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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