论文标题
Maestro-X：旋转翼无人机群的分布式编排
MAESTRO-X: Distributed Orchestration of Rotary-Wing UAV-Relay Swarms
论文作者
论文摘要
这项工作详细介绍了一个可扩展的框架，以协调一群旋转翼无人机，可作为蜂窝继电器，以促进地面用户的视线连接和流量卸载。首先，为单个无人机开发了多尺度自适应能量的调度和轨迹优化（Maestro）框架。为了最大程度地减少服务用户请求的时间平均延迟，但要受到普通无人机功率约束，这表明可以将优化问题作为半马尔可夫决策过程施加，并表现出多尺度结构：对径向候补速度和终端服务的外部动作，最小化长期延迟驱动器的位置通过价值最小化，以最大程度地减少通过价值进行优化的价值效率；鉴于这些外部动作，对角式等待速度的内部作用和服务轨迹最大程度地减少了短期延迟能量成本。在内部优化中开发了一种新型的分层竞争优化方案，以通过迭代的配对更新来设计高分辨率轨迹。接下来，Maestro通过可扩展的策略复制扩展到无人机群（Maestro-X）：由分散的命令和控制网络启用，最佳的单代代理策略通过扩展最大化，共识驱动的冲突解决方案，适应性频率重复使用和PiggyBackanging增强。数值评估表明，对于用户请求10 mbits，根据Poisson到达过程生成的速率为0.2 req/min/uav，单代理大师提供的服务比静态UAV部署更快3.8倍，比高空平台快29％。此外，对于由3个无人机组成的群体，Maestro-X的数据有效载荷的速度比连续的凸近似方案快4.7倍。值得注意的是，通过Maestro Out-Out-Out-Out-Out-Qunet网络优化了38％的单一UAV。
This work details a scalable framework to orchestrate a swarm of rotary-wing UAVs serving as cellular relays to facilitate beyond line-of-sight connectivity and traffic offloading for ground users. First, a Multiscale Adaptive Energy-conscious Scheduling and TRajectory Optimization (MAESTRO) framework is developed for a single UAV. Aiming to minimize the time-averaged latency to serve user requests, subject to an average UAV power constraint, it is shown that the optimization problem can be cast as a semi-Markov decision process, and exhibits a multiscale structure: outer actions on radial wait velocities and terminal service positions minimize the long-term delay-power trade-off, optimized via value iteration; given these outer actions, inner actions on angular wait velocities and service trajectories minimize a short-term delay-energy cost. A novel hierarchical competitive swarm optimization scheme is developed in the inner optimization, to devise high-resolution trajectories via iterative pair-wise updates. Next, MAESTRO is eXtended to UAV swarms (MAESTRO-X) via scalable policy replication: enabled by a decentralized command-and-control network, the optimal single-agent policy is augmented with spread maximization, consensus-driven conflict resolution, adaptive frequency reuse, and piggybacking. Numerical evaluations show that, for user requests of 10 Mbits, generated according to a Poisson arrival process with rate 0.2 req/min/UAV, single-agent MAESTRO offers 3.8x faster service than a high-altitude platform and 29% faster than a static UAV deployment; moreover, for a swarm of 3 UAV-relays, MAESTRO-X delivers data payloads 4.7x faster than a successive convex approximation scheme; and remarkably, a single UAV optimized via MAESTRO outclasses 3 UAVs optimized via a deep-Q network by 38%.
