A distributed four-dimensional (4D) trajectory generation method based on multi-agent Q learning is presented for multiple unmanned aerial vehicles (UAVs). Based on this method, each vehicle can intelligently generate collision-free 4D trajectories for time-constrained cooperative flight tasks. For a single UAV, the 4D trajectory is generated by the bionic improved tau gravity guidance strategy, which can synchronously guide the position and velocity to the desired values at the arrival time. Furthermore, to optimise trajectory parameters, the continuous state and action wire fitting neural network Q (WFNNQ) learning method is applied. For multi-UAV applications, the learning is organised by the win or learn fast-policy hill climbing (WoLF-PHC) algorithm. Dynamic simulation results show that the proposed method can efficiently provide 4D trajectories for the multi-UAV system in challenging simultaneous arrival tasks, and the fully trained method can be used in similar trajectory generation scenarios.

中文翻译：

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基于多智能体 Q 学习的无人机四维轨迹生成

针对多无人机（UAV）提出了一种基于多智能体Q学习的分布式四维（4D）轨迹生成方法。基于这种方法，每辆车都可以智能地生成无碰撞的 4D 轨迹，用于时间受限的协作飞行任务。对于单架无人机，4D轨迹是通过仿生改进的tau重力引导策略生成的，可以在到达时将位置和速度同步引导到期望值。此外，为了优化轨迹参数，应用了连续状态和动作线拟合神经网络 Q (WFNNQ) 学习方法。对于多无人机应用，学习由 win or learn 快速策略爬山 (WoLF-PHC) 算法组织。