针对无人机的航迹规划问题, 提出了并联神经网络结构与动态可调步长策略相结合的三维航迹规划方法。首先根据与威胁之间的距离采取不同策略:当无人机处于威胁区域外, 采取基准步长为大步长的策略, 实现快速生成航迹的目的；当无人机处于威胁区域内, 采取可调步长的策略, 实现航迹的精细搜索。然后构建障碍物惩罚函数的神经网络和航迹的能量函数, 将梯度下降法与牛顿下山法相结合, 建立航迹的运动方程, 依据不同的航迹点, 采用自适应学习因子不同的学习率, 实现快速脱离威胁。仿真结果表明, 所提算法不仅能保证无人机安全绕开威胁, 同时也提高了算法的收敛速度。

Aiming at the problem of UAV path planning, a 3D path planning method is proposed based on the parallel neural network structure and the dynamic adjustable step size strategy.Firstly, different strategies are adopted according to the distance between the UAV and the threat.When the UAV is outside the risk area, the method of taking large steps is adopted to achieve the purpose of rapid generation of the path.When the UAV is inside the risk area, the method of taking the adjustable step is used to achieve the fine search of the path.Then, the neural network of the obstacles penalty function and the energy function of the path are constructed.By combining the gradient descent method with the Newton Downhill Method, the motion equation of the path is established.According to different path points, learning rates with different adaptive learning factors are used to realize rapid escaping from the threats.The simulation results show that the proposed algorithm not only guarantees the safety of UAV to bypass the threat, but also improves the convergence speed of the algorithm.