Path Planning Research of a UAV Base Station Searching for Disaster Victims' Location Information Based on Deep Reinforcement Learning

• Published in: Entropy (Basel, Switzerland) Switzerland), 2022-12, Vol.24 (12), p.1767
• Main Authors: Zhao, Jinduo, Gan, Zhigao, Liang, Jiakai, Wang, Chao, Yue, Keqiang, Li, Wenjun, Li, Yilin, Li, Ruixue
• Format: Article
• Language: English
• Subjects: Aircraft; Algorithms; Cellular telephone equipment industry; Communication; Communications networks; Decision making; Decision theory; Deep learning; deep reinforcement learning; Design; Disaster relief; Disaster victims; Disasters; Drone aircraft; Efficiency; Employee motivation; Evacuations & rescues; Internet of Things; Machine learning; Optimization; Path planning; Planning; received signal strength; Signal strength; Simulation; Simulation methods; Splitting; UAV base station; Unmanned aerial vehicles; Wireless telecommunications equipment
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e24121767

Aiming at the path planning problem of unmanned aerial vehicle (UAV) base stations when performing search tasks, this paper proposes a Double DQN-state splitting Q network (DDQN-SSQN) algorithm that combines state splitting and optimal state to complete the optimal path planning of UAV based on the Deep Reinforcement Learning DDQN algorithm. The method stores multidimensional state information in categories and uses targeted training to obtain optimal path information. The method also references the received signal strength indicator (RSSI) to influence the reward received by the agent, and in this way reduces the decision difficulty of the UAV. In order to simulate the scenarios of UAVs in real work, this paper uses the Open AI Gym simulation platform to construct a mission system model. The simulation results show that the proposed scheme can plan the optimal path faster than other traditional algorithmic schemes and has a greater advantage in the stability and convergence speed of the algorithm.