UAV online path planning technology based on deep reinforcement learning

This paper proposes a method for planning three-dimensional path for unmanned aerial vehicle (UAV) in complex airspace based on interfered fluid dynamical system (IFDS) and deep reinforcement learning. Firstly, the model of unmanned aerial vehicle under various constraints and the mathematical expression of threat zone are established. Secondly, in order to solve the problems of slow calculation speed and difficult to make the global optimal solution existed at present, an intelligent 3D path planning method on the basis of IFDS is proposed, and deep reinforcement learning is used to solve the coefficient of IFDS. The simulation results show that the path planned by the proposed method can avoid the threat zone effectively, meanwhile, the path is smooth, suitable and fuel saving for UAV.