In an unmanned aerial vehicle ad-hoc network (UANET), sparse and rapidly mobile unmanned aerial vehicles (UAVs)/nodes can dynamically change the UANET topology. This may lead to UANET service performance issues. In this study, for planning rapidly changing UAV swarms, we propose a dynamic value iteration network (DVIN) model trained using the episodic Q-learning method with the connection information of UANETs to generate a state value spread function, which enables UAVs/nodes to adapt to novel physical locations. We then evaluate the performance of the DVIN model and compare it with the non-dominated sorting genetic algorithm II and the exhaustive method. Simulation results demonstrate that the proposed model significantly reduces the decision-making time for UAV/node path planning with a high average success rate.

在无人飞行器自组织网络（UANET）中，稀疏且快速移动的无人飞行器（UAV）/节点可以动态更改UANET拓扑。这可能会导致UANET服务性能问题。在这项研究中，为了规划快速变化的无人机群，我们提出了一种使用情节Q学习方法训练的动态值迭代网络（DVIN）模型，并结合了UANET的连接信息来生成状态值扩展函数，从而使无人机/节点能够适应新颖的地理位置。然后，我们评估DVIN模型的性能，并将其与非主导排序遗传算法II和穷举方法进行比较。仿真结果表明，所提出的模型以较高的平均成功率显着减少了无人机/节点路径规划的决策时间。