基于深度强化学习的UAV航路自主引导机动控制决策算法

针对无人机(unmanned aerial vehicle, UAV)航路终端约束情况下航路自主引导机动控制决策问题,采用Markov决策过程模型建立UAV自主飞行机动模型,基于深度确定性策略梯度提出UAV航路自主引导机动控制决策算法,拟合UAV航路自主引导机动控制决策函数与状态动作值函数,生成最优决策网络,开展仿真验证。仿真结果表明,该算法实现了UAV在任意位置/姿态的初始条件下,向航路目标点的自主飞行,可有效提高UAV机动控制的自主性。

Autonomous guidance maneuver control and decision-making algorithm

To solve a specific problem involved in autonomous guidance maneuver control of the unmanned aerial vehicle (UAV) route under terminal position constraints, the autonomous flight model of the UAV is described based on Markov decision processes and the simulation environment for the training algorithm is constructed. Meanwhile, an autonomous guidance maneuver control algorithm of UAV is proposed based on deep deterministic policy gradient (DDPG) and the guidance maneuvering control function and the state-action value function are fitted by the neural network. Finally, the simulation results show that the UAV using the proposed algorithm can fly to a fixed position in horizontal plane from any position and attitude. It is proved that the proposed algorithm can effectively improve the autonomy of the UAV.