用于灾害响应无人机集群中战略性物联网部署覆盖的元强化学习
Meta Reinforcement Learning for Strategic IoT Deployments Coverage in Disaster-Response UAV Swarms
AI总结：
针对灾害响应中无人机集群服务战略物联网位置的能耗与路径规划难题，提出轻量级元强化学习方法，实现快速收敛并优于三种现有学习算法。
摘要：
在过去十年中，无人机（UAV）凭借其在机动性和移动灵活性方面的优势，在为地面用户提供无线服务、从受灾害影响地区收集数据等关键应急应用中的潜在用途，吸引了学术界和工业界研究人员的关注。无人机有限的资源、能量预算以及严格的任务完成时间，给在这些应用中采用无人机带来了挑战。我们的系统模型考虑一个无人机集群在某区域内导航，从地面物联网设备收集数据，重点是为战略位置提供更好的服务，并允许无人机以动态方式加入和离开集群（例如为了充电）。在本工作中，我们提出一个优化模型，旨在最小化总能耗，并在最短完成时间和发射功率约束下提供无人机的最优路径规划。所构建的优化问题是NP难的，因此不适用于实时决策。为此，我们提出一种轻量级元强化学习解决方案，该方案还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，并将所提方法与三种最先进的学习模型进行比较。仿真结果证明，在以快速收敛为战略位置提供覆盖方面，我们提出的方法优于这三种最先进的算法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
