Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı Tez Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/418

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Browsing Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı Tez Koleksiyonu by Author "Alabay, Hüsnü Halid"

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    Coordinated target detection and tracking by drones using distance and vision
    (Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2022) Alabay, Hüsnü Halid; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
    Robot autonomy refers to the ability to carry out objectives by perceiving the environment and deciding on the actions required without human interruption. Although autonomous aerial robots offer big advantages in our daily life, online localization and control remain the biggest challenge lying ahead of aerial robot implementations. For single robot applications, GPS, and motion capture (mocap) systems can be utilized for outdoor and indoor applications, respectively. However, when it comes to multi-robot systems, the relative localization problem needs to be solved beyond the single robot localization problem. Furthermore, GPS signals are not available everywhere, and mocap systems limit the application space of multi-robot systems. Motivated by the industrial application scenarios, we address the relative localization and docking problem in multi-drone systems where drones do not utilize any external infrastructure for localization. We consider a two-drone system that aims at docking a target object which consists of an ultrawideband (UWB) distance sensor. The drones are equipped with UWB sensors and cameras and try to localize the target object and dock around it in a pre-defined configuration in the absence of GPS and magnetometer sensors and external infrastructures. We design an extended Kalman filter based on the dynamic model of the drone-target configuration that fuses the distance and vision sensor outputs. Particularly, we use the YOLO algorithm for the bearing detection between the drones and the target. Next, we devise and implement a switching-based distributed formation control algorithm and integrate it into the estimation algorithm. We demonstrate the performance of our algorithm in several simulation studies in a realistic Gazebo environment. Finally, we provide primary experimental results and a roadmap to the full implementation of the system.