Route Following of Quadrotor UAV Based on Deep Reinforcement Learning
YANG Zhipeng1, 2 LI Bo1, 3 GAN Zhigang1 LIANG Shiyang4
1. School of Electronic Information, Northwestern Polytechnical University, Xi’an Shaanxi 710072, China 2. System Design Institute, Hubei Aerospace Technology Academy, Wuhan Hubei 430040, China 3. Key Laboratory of Data Link Technology, China Electronics Technology Group Corporation, Xi’an Shaanxi 710077, China 4. China Luoyang Institute of Electro-optical Equipment of AVIC, Luoyang Henan 471000, China
Abstract:In view of the problem that the unmanned aerial vehicle (UAV)is unable to take reasonable countermeasures in the stochastic external environment during executing the mission in the air, an approach of UAV route following based on deep reinforcement learning is proposed. The quadrotor UAV dynamics model is found by force analysis of UAV and transformation of Euler angle. Under the framework of deep reinforcement learning, such relative factors of UAV as the coordinates, the Euler angles, the flight velocity, etc. are analyzed, the state space is fuzzified as the state input of deep reinforcement learning. Compared with the traditional method, the buile non-linear flight dynamics and dynemic model of the quadrotor UAV is more realistic. The simulation results show that the quadrotor UAV can perform the task of randomly generated route following with high efficiency and low error after continuous training.
杨志鹏, 李波, 甘志刚,梁诗阳. 基于深度强化学习的四旋翼无人机航线跟随[J]. 指挥与控制学报, 2022, 8(4): 477-482.
YANG Zhipeng, LI Bo, GAN Zhigang, LIANG Shiyang. Route Following of Quadrotor UAV Based on Deep Reinforcement Learning. Journal of Command and Control, 2022, 8(4): 477-482.