부산대학교 도서관

It is essential to locate the victims timely for efficient rescue after the disaster occurs in the global positioning system (GPS) denied forest area due to the influence of the tree shading. Existing works have studied optimizing the trajectory of the unmanned aerial vehicle (UAV) to exploit the wireless signal from the ground users for localization. However, current works mainly focus on optimizing the localization accuracy while paying limited attention to the localization task completion time, which makes it challenging to meet the timeliness requirements of emergency rescue missions. To provide accurate localization services for the ground victims quickly, we propose a multi-agent deep reinforcement learning (MA-DRL)-based UAV trajectory planning algorithm, which can provide high-efficiency cooperation between the multiple UAVs by exploiting the prior information and measurements from the partners. Specifically, a low-resolution trajectory planning algorithm is proposed to reduce redundant flight distances in the pre-fly stage to localize the victim’s quantity and dispersion. Furthermore, to provide high localization accuracy and energy-efficiency victims location services quickly, we exploit the coarse user information from the pre-fly stage, integrate an adaptive forest channel model and UAV energy consumption model, and propose an MA-DRL-based UAV trajectory planning algorithm which can perform decentralized execution for the high-efficiency cooperation localization. Simulation results show that our method can finish localization missions faster with less energy consumption while guaranteeing the localization accuracy compared to other benchmark algorithms.