Autonomous drone hunter operating by deep learning and all-onboard computations in GPS-denied environments is a report by
This paper introduces a UAV platform designed for autonomous detection, pursuit, and neutralization of other small UAVs in GPS-denied environments. The platform employs a pre-trained machine learning model to detect, track, and follow a target drone within its sensor range. A comprehensive dataset of 58,647 images is collected and generated for training a Tiny YOLO detection algorithm. The validation of this algorithm, coupled with a straightforward visual-servoing approach, is performed on a physical platform. The results demonstrate the platform’s capability to effectively track and follow a target drone at an estimated speed of 1.5 m/s. However, performance is constrained by the detection algorithm’s 77% accuracy in cluttered environments, an eight frames-per-second frame rate, and the camera’s limited field of view.
Publication Date– November 2019
Autonomous drone hunter operating by deep learning and all-onboard computations in GPS-denied environments contains the following major sections:
- Introduction
- Related Work
- Contributions
- Structure of the Paer
- Materials and Methods
- Results
- Discussion
- Conclusion
Open Access Research
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Post Image- Hunter drone prototype (Image Credit: Authors)
