Platooning control of drones with real-time deep learning object detection

摘要

In this short paper, we study platooning control of drones using only the information from a camera attached to each drone. For this, we adopt real-time objection detection based on a deep learning model called YOLO (you only look once). The YOLO object detector continuously estimates the relative position of the drone in front, by which each drone is controlled by a PD (Proportional-Derivative) feedback controller for platooning. The effectiveness of the proposed system is shown by indoor experiments with three drones.