基于气象威胁场的无人机三维航迹规划

针对无人机航迹规划中气象威胁要素模糊性强、不确定性高等特点,提出了改进的BP神经网络的气象威胁度评估方法,对区域内气象威胁要素进行评估以建立表征气象威胁的气象威胁场。在所建立的气象威胁场上采用改进稀疏A*算法进行三维航迹规划,改进稀疏A*算法通过引入自适应操作提高了收敛速度以及效率。仿真结果表明,这种威胁评估方法可较为准确地评估区域内的气象威胁,改进稀疏A*算法能够准确、快速地在三维气象威胁场上寻找到最优航迹,具有一定的应用价值。

3D route planning of UAVs based on weather threat field

To tackle the ambiguity and high degree of uncertainty of the weather threats in UAV route planning, a method was proposed that uses the improved BP neural network to assess the weather element, and assesses the weather threats within the area to build the weather threat field. The improved spares A* algorithm was proposed to accomplish 3D route planning based on the integrated weather threat field, and the adaptive operation was used in the improved spares A</sup>* algorithm to get a higher searching speed and efficiency. The simulation results show that the assessing method can assess the degree of weather threat accurately, and that the improved spares A</sup>* algorithm can search the best route accurately and fast in the 3D weather threat field.This method is of some practical value.</sup>