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

Author

Listed:
  • Philippe Martin Wyder
  • Yan-Song Chen
  • Adrian J Lasrado
  • Rafael J Pelles
  • Robert Kwiatkowski
  • Edith O A Comas
  • Richard Kennedy
  • Arjun Mangla
  • Zixi Huang
  • Xiaotian Hu
  • Zhiyao Xiong
  • Tomer Aharoni
  • Tzu-Chan Chuang
  • Hod Lipson

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.

Suggested Citation

  • Philippe Martin Wyder & Yan-Song Chen & Adrian J Lasrado & Rafael J Pelles & Robert Kwiatkowski & Edith O A Comas & Richard Kennedy & Arjun Mangla & Zixi Huang & Xiaotian Hu & Zhiyao Xiong & Tomer Aha, 2019. "Autonomous drone hunter operating by deep learning and all-onboard computations in GPS-denied environments," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-18, November.
    • Handle: RePEc:plo:pone00:0225092
    DOI: 10.1371/journal.pone.0225092
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