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Autonomous drone hunter operating by deep learning and all-onboard computations in GPS-denied environments
- Philippe M. Wyder
- Yan-Song Chen
- Adrian J. Lasrado
- Rafael J. Pelles
- Robert Kwiatkowski
- Edith O. A. Comas
- Richard Kennedy
- Arjun Mangla
- Zixi Huang
- Xiaotian Hu
- Tomer Aharoni
- Tzu-Chan Chuang
- Hod Lipson
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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.
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