산 너머로 드론

Unmanned aerial vehicle (UAV) is widely used for military, delivery, and disaster because of many advantages of network formation, high mobility, and low cost. It is expected that the UAV control technique for target tracking is essential because UAV collects environmental knowledge and provides an effective wireless network to ground users. We have been studying deep reinforcement learning (DRL) based on multiple UAV control for target (i.e., ground user) tracking. DRL is an emerging tool to select optimal action in a dynamic system, for example, autonomous driving, UAV control problem and wireless network, etc. By applying DRL to multiple UAVs system, UAVs learn how to take actions to track/localize the ground target in a complex 3D environment. Besides, the DRL-based UAV control technique mitigates computation time that becomes critical when the number of UAVs or targets increases.

Fig 1. Multiple UAVs track ground users in challenging 3D environment

Multiple UAVs are assigned to tracking missions in the existence of multiple ground targets. Each of the UAVs is equipped with a range sensor that measures the distance between UAVs and targets. The measurements received by UAVs are blocked due to obstacles and structure.

Fig 2. The results of  3D trajectories of UAV and target (left), and localization error (right) during the tracking missions

While operating tracking missions, UAV’s flight direction is determined by the trained-DRL network. Tracking error measures MSE between the true target position and estimated target position. Applying DRL to target tracking enables UAVs to track and localize ground users accurately.

김선우 교수

한양대학교 융합전자공학부

서울특별시 성동구 왕십리로 한양대학교, 04763

교수연구실: IT/BT관 817호 T) +82-2-2220-4823

학생연구실: 퓨전테크센터 516호

​행정실: IT/BT관 822호 T) +82-2-2220-4822

Professor Sunwoo Kim

Dept. of Electronic Engineering, Hanyang University

222 Wangsimri-ro Seongdong-gu Seoul Korea, 04763

T) +82-2-2220-4822