When an earthquake occurs, disaster relief is an urgent, complex and critical mission. High on the list is communication network recovery within the disaster area. Unmanned aerial vehicles (UAVs) are often used in this regard. Some of them are used as collective repeaters to provide the required network coverage. Their timely, efficient, and collaborative deployment to specific locations is a big challenge. To meet this challenge, this paper formalizes and solves the problem of UAV deployment for signal relays via group role assignment (GRA). The minimum spanning tree algorithm is used to model a rapidly deployed optimal relay network. It can help establish the minimum number of relay points necessary to ensure communication stability. In this scenario, UAVs (agents) adopt roles as communication relays. The task of distributing UAVs to relay points can be solved quickly via the assignment process of GRA, which can solve the x-ILP problem with the help of the PuLP package of Python. Results from thousands of experimental simulations indicate that our solutions are effective, robust and practical. The process can be used to establish an optimal, efficient, and collaborative relay network using UAVs. Their rapid deployment can be a significant contribution to earthquake disaster relief.

中文翻译：

---

通过组角色分配将 UAV 分配为救灾中的无线中继器

地震发生时，救灾是一项紧迫、复杂和关键的任务。排在首位的是灾区的通信网络恢复。在这方面经常使用无人驾驶飞行器（UAV）。其中一些用作集体中继器以提供所需的网络覆盖。他们及时、高效、协作地部署到特定位置是一个巨大的挑战。为了应对这一挑战，本文通过组角色分配（GRA）形式化并解决了无人机部署信号中继的问题。最小生成树算法用于对快速部署的最优中继网络进行建模。它可以帮助建立确保通信稳定性所需的最小中继点数量。在这种情况下，无人机（代理）扮演通信中继的角色。将无人机分发到中继点的任务可以通过 GRA 的分配过程快速解决，它可以借助 Python 的 PuLP 包解决 x-ILP 问题。数千次实验模拟的结果表明，我们的解决方案是有效、稳健和实用的。该过程可用于使用无人机建立最佳、高效和协作的中继网络。它们的快速部署可以为地震救灾做出重大贡献。