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

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 and Hod Lipson

PLOS ONE, 2019, vol. 14, issue 11, 1-18

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.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0225092

DOI: 10.1371/journal.pone.0225092

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