State-of-the-art drone technologies have severe flight time limitations due to weight constraints, which inevitably lead to a relatively small amount of available energy. Therefore, frequent battery replacement or recharging is necessary in applications such as delivery, exploration, or support to the wireless infrastructure. Mobile charging stations (i.e., mobile stations with charging equipment) for outdoor ad-hoc battery charging is one of the feasible solutions to address this issue. However, the ability of these platforms to charge the drones is limited in terms of the number and charging time. This paper designs an auction-based mechanism to control the charging schedule in multi-drone setting. In this paper, charging time slots are auctioned, and their assignment is determined by a bidding process. The main challenge in developing this framework is the lack of prior knowledge on the distribution of the number of drones participating in the auction. Based on optimal second-price-auction, the proposed formulation, then, relies on deep learning algorithms to learn such distribution online. Numerical results from extensive simulations show that the proposed deep learning-based approach provides effective battery charging control in multi-drone scenarios.

中文翻译：

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多无人机网络深度学习框架基于拍卖的计费调度

由于重量限制，最先进的无人机技术具有严重的飞行时间限制，这不可避免地导致可用能量相对较少。因此，在无线基础设施的交付、探索或支持等应用中，需要频繁更换电池或充电。用于室外临时电池充电的移动充电站（即带有充电设备的移动站）是解决该问题的可行方案之一。然而，这些平台为无人机充电的能力在数量和充电时间方面都受到限制。本文设计了一种基于拍卖的机制来控制多无人机设置中的充电时间表。在本文中，充电时隙是拍卖的，它们的分配由投标过程确定。开发该框架的主要挑战是缺乏对参与拍卖的无人机数量分布的先验知识。基于最优的二次价格拍卖，所提出的公式依赖于深度学习算法来在线学习这种分布。大量模拟的数值结果表明，所提出的基于深度学习的方法在多无人机场景中提供了有效的电池充电控制。