Research and Simulation of UAV Three-Dimensional Path Replanning in Complex Environment

In order to make the research of unmanned aerial vehicle (UAV) path replanning closer to the actual combat environment, a path replanning algorithm for UAV based on model-based predictive control and particle swarm optimization is proposed to study the three-dimensional (3D) path replanning problem in situations involving multiple kinds of threats. Firstly, the interactive model of constant velocity (CV) model and coordinated turn (CT) model is used to describe the motion state of the sudden threat, and the Kalman filter method is used to predict the state of the sudden threat, considering the systematic error in the movement process of the sudden threat and the detection error of the airborne radar. Then, based on the 3D dynamics model of UAV, a 3D path planning model is established, and the path replanning problem is transformed into a series of optimization sub-problems by using the idea of model-based predictive control (MPC). Finally, the sub-problem is solved by using particle swarm optimization (PSO) algorithm. The experimental results show that the trajectory prediction of motion sudden threat in this paper is more accurate, and the designed algorithm not only meets the validity and real-time requirements of path replanning, but also has strong robustness.