In the past decade, Unmanned Aerial Vehicles (UAVs) have grabbed the attention of researchers in academia and industry for their potential use in critical emergency applications, such as providing wireless services to ground users and collecting data from areas affected by disasters, due to their advantages in terms of maneuverability and movement flexibility. The UAVs' limited resources, energy budget, and strict mission completion time have posed challenges in adopting UAVs for these applications. Our system model considers a UAV swarm that navigates an area collecting data from ground IoT devices focusing on providing better service for strategic locations and allowing UAVs to join and leave the swarm (e.g., for recharging) in a dynamic way. In this work, we introduce an optimization model with the aim of minimizing the total energy consumption and provide the optimal path planning of UAVs under the constraints of minimum completion time and transmit power. The formulated optimization is NP-hard making it not applicable for real-time decision making. Therefore, we introduce a light-weight meta-reinforcement learning solution that can also cope with sudden changes in the environment through fast convergence. We conduct extensive simulations and compare our approach to three state-of-the-art learning models. Our simulation results prove that our introduced approach is better than the three state-of-the-art algorithms in providing coverage to strategic locations with fast convergence.

翻译：在过去十年中，无人驾驶飞行器（UAV）凭借其机动性和移动灵活性优势，在关键应急应用（如为地面用户提供无线服务及从受灾区域采集数据）中的潜在用途，吸引了学术界和工业界研究人员的关注。然而，无人机有限的资源、能源预算以及严格的任务完成时间，对其在这些应用中的采用提出了挑战。我们的系统模型考虑了一个无人机集群，该集群在区域中导航以从地面物联网设备采集数据，重点为战略位置提供更优服务，并允许无人机以动态方式加入或离开集群（例如，用于充电）。在本研究中，我们提出一个优化模型，旨在最小化总能量消耗，并在最小完成时间和发射功率约束下提供无人机的最优路径规划。所提出的优化问题属于NP-hard问题，因此不适用于实时决策。为此，我们引入了一种轻量级元强化学习解决方案，该方案能够通过快速收敛应对环境中的突发变化。我们进行了大量仿真，并将我们的方法与三种最先进的学习模型进行了比较。仿真结果证明，我们所提出的方法在快速收敛的同时，为战略位置提供覆盖的能力优于这三种最先进的算法。