Autonomous drone hunter operating by deep learning and all-onboard computations in GPS-denied environments

  1. Yan-Song Chen
  2. Adrian J. Lasrado
  3. Rafael J. Pelles
  4. Edith O. A. Comas
  5. Arjun Mangla
  6. Xiaotian Hu
  7. Tomer Aharoni
  8. Tzu-Chan Chuang

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Category: Data

Description: This data repository hosts relevant data to our paper with the same name. Paper Abstract: This paper proposes a UAV platform that autonomously detects, hunts, and takes down other small UAVs in GPS-denied environments. The platform detects, tracks, and follows another drone within its sensor range using a pre-trained machine learning model. We collect and generate a 58,647-image dataset and use it to train a Tiny YOLO detection algorithm. This algorithm combined with a simple visual-servoing approach was validated on a physical platform. Our platform was able to successfully track and follow a target drone at an estimated speed of 1.5 m/s. Performance was limited by the detection algorithm’s 77% accuracy in cluttered environments and the frame rate of eight frames per second along with the field of view of the camera.

License: BSD 2-Clause "Simplified" License


Autonomous drone hunter operating by deep learning and all-onboard computations in GPS-denied environments Philippe Martin Wyder1*, Yan-Song Chen2, Adrian J. Lasrado1, Rafael J. Pelles1, Robert Kwiatkowski2, Edith O. A. Comas2, Richard Kennedy2, Arjun Mangla2, Zixi Huang3, Xiaotian Hu3, Zhiyao Xiong1, Tomer Aharoni2, Tzu-Chan Chuang2, Hod Lipson1 1Department of Mechanical Engineering, Columbia Uni...


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