论文标题

优化用于异质和持续航空服务的无人机补给计划

Optimizing UAV Recharge Scheduling for Heterogeneous and Persistent Aerial Service

论文作者

Arribas, Edgar, Cholvi, Vicent, Mancuso, Vincenzo

论文摘要

由于部署了将无人机连接并将其用作通信继电器,因此在通信网络中采用无人机正在成为现实。但是,由于需要回电和充电或替换能量用光的无人机,因此使用无人机在网络设备的动态管理中引入了新的能量限制和调度挑战。在本文中,我们根据有限的飞行时间和耗时的充电操作设计了无人机补给计划。这样的方案旨在最大程度地减少舰队的规模,以致力于一组空中位置的持续服务,因此其成本。我们认为在同质和异质的服务拓扑下的均匀无人机舰队。对于将空中位置提供均匀距离的无人机,我们设计了一个简单的安排,我们将恐怖命名为“恐怖”,我们证明这是可行且最佳的,从某种意义上说,它使用了最小可能数量的无人机来保证航空服务位置的覆盖范围。对于非分布的空中位置的情况,我们证明了问题变成了NP-HARD,并设计了一种轻巧的充电调度方案Pherr,该方案将恐怖的操作扩展到了异质案例,利用了服务位置集的分区。我们表明,菲尔(Pherr)几乎是最佳的,因为它通过我们在总舰队大小上制定的下限来识别出的性能极限。

The adoption of UAVs in communication networks is becoming reality thanks to the deployment of advanced solutions for connecting UAVs and using them as communication relays. However, the use of UAVs introduces novel energy constraints and scheduling challenges in the dynamic management of network devices, due to the need to call back and recharge, or substitute, UAVs that run out of energy. In this paper, we design UAV recharging schemes under realistic assumptions on limited flight times and time consuming charging operations. Such schemes are designed to minimize the size of the fleet to be devoted to a persistent service of a set of aerial locations, hence its cost. We consider a fleet of homogeneous UAVs both under homogeneous and heterogeneous service topologies. For UAVs serving aerial locations with homogeneous distances to a recharge station, we design a simple scheduling, that we name HORR, which we prove to be feasible and optimal, in the sense that it uses the minimum possible number of UAVs to guarantee the coverage of the aerial service locations. For the case of non-evenly distributed aerial locations, we demonstrate that the problem becomes NP-hard, and design a lightweight recharging scheduling scheme, PHERR, that extends the operation of HORR to the heterogeneous case, leveraging the partitioning of the set of service locations. We show that PHERR is near-optimal because it approaches the performance limits identified through a lower bound that we formulate on the total fleet size.

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