Due to the small size, low cost, and autonomous nature, unmanned aerial vehicles (UAVs) have attracted growing attention in improving traditional networks’ performance. This paper considers a multi-UAV enabled large-scale wireless recharge networks, where a group of UAVs is dispatched as mobile wireless power transfer, and information collection systems to serve a set of ground low-power sensor nodes. Specifically, the UAVs employ radio frequency (RF) wireless power transfer (WPT) to supply energy towards the sensor nodes, and the communication services are available via wireless information transfer (WIT) systems. To achieve balanced energy consumption among UAVs, we maximize the energy utilization efficiency of UAVs, and minimize the communication delay by optimizing the trajectory jointly with constraints of the energy capacity, and the area of the target region. The formulated problem is a mixed-integer programming problem that is a variation of multiple traveling salesman problem. Thus we present a heuristic algorithm that combines the evolutionary algorithm, and variable neighborhood search to achieve the optimal visited sequence of the sensor nodes. Finally, extensive numerical results are provided to evaluate the performance of the proposed algorithm. It draws new insights on the estimation of the feasibility of the given UAVs whose energy capacity is limited.

中文翻译：

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多无人机辅助大规模无线充电网络中的负载平衡和轨迹设计

由于体积小、成本低和自主性，无人驾驶飞行器（UAV）在提高传统网络性能方面引起了越来越多的关注。本文考虑了一个支持多无人机的大规模无线充电网络，其中一组无人机被调度为移动无线电力传输，以及信息收集系统为一组地面低功耗传感器节点提供服务。具体而言，无人机采用射频 (RF) 无线电力传输 (WPT) 向传感器节点提供能量，并且通过无线信息传输 (WIT) 系统提供通信服务。为了实现无人机之间的能量消耗均衡，我们最大化无人机的能量利用效率，并通过优化轨迹结合能量容量的约束来最小化通信延迟，以及目标区域的面积。公式化问题是一个混合整数规划问题，它是多旅行商问题的变体。因此，我们提出了一种结合进化算法和可变邻域搜索的启发式算法，以实现传感器节点的最佳访问序列。最后，提供了大量的数值结果来评估所提出算法的性能。它为估计能量容量有限的给定无人机的可行性提供了新的见解。和变量邻域搜索以实现传感器节点的最佳访问序列。最后，提供了大量的数值结果来评估所提出算法的性能。它为估计能量容量有限的给定无人机的可行性提供了新的见解。和变量邻域搜索以实现传感器节点的最佳访问序列。最后，提供了大量的数值结果来评估所提出算法的性能。它为估计能量容量有限的给定无人机的可行性提供了新的见解。