【摘要】 在无人机自组网(UANET)中,稀疏且高速移动的无人机节点会动态改变无人机自组网的拓扑结构,这可能会导致无人机自组网服务性能问题。为规划快速变化的无人机群,本文提出一种动态值迭代网络(DVIN)模型,该模型利用无人机自组网的连接信息,采用场景式Q学习方法训练,生成状态值传播函数,使无人机节点能够自适应调节至新的物理位置。然后,评估了动态值迭代网络模型的性能,并将其与非支配排序遗传算法NSGA-Ⅱ和穷举法比较。仿真结果表明,动态值迭代网络模型显著缩短了无人机节点路径规划的决策时间,且平均成功率更高。更多还原

【Abstract】 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 decisionmaking time for UAV/node path planning with a high average success rate.更多还原