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


Autoři: Philippe Martin Wyder aff001;  Yan-Song Chen aff002;  Adrian J. Lasrado aff001;  Rafael J. Pelles aff001;  Robert Kwiatkowski aff002;  Edith O. A. Comas aff002;  Richard Kennedy aff002;  Arjun Mangla aff002;  Zixi Huang aff003;  Xiaotian Hu aff003;  Zhiyao Xiong aff001;  Tomer Aharoni aff002;  Tzu-Chan Chuang aff002;  Hod Lipson aff001
Působiště autorů: Department of Mechanical Engineering, Columbia University, New York, New York, United States of America aff001;  Department of Computer Science, Columbia University, New York, New York, United States of America aff002;  Department of Electrical Engineering, Columbia University, New York, New York, United States of America aff003
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: 10.1371/journal.pone.0225092

Souhrn

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.

Klíčová slova:

Algorithms – Cameras – Computer imaging – Computers – Flight testing – Machine learning algorithms – Neural networks – Target detection


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Článek vyšel v časopise

PLOS One


2019 Číslo 11