A semi-autonomous flight and object recognition program was explored by two undergraduate researchers to navigate the 2022 DASC Drone Competition course and detect ground-based yellow rubber ducks utilizing a Tello EDU μdrone Unmanned Aerial System (UAS). The research team used several programs and libraries to accomplish the aforementioned tasks namely: Python and OpenCV-Python. Initially, the research team did not succeed in developing a fully autonomous flight and object detection program due to hardware limitations within the microdrone. After several engineering iterations, the research team was able to achieve consistent successful semi-autonomous flight and object detection using the UAS and associated personal hardware.


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    Titel :

    Development of Semi-Autonomous Flight & Detection Systems Using Small Drones


    Beteiligte:
    Lane, Chyna (Autor:in) / Jones, Orion (Autor:in) / Miloro, Christopher (Autor:in) / Frye, Michael (Autor:in)


    Erscheinungsdatum :

    18.09.2022


    Format / Umfang :

    1930794 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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