Multiscale Adaptive Scheduling and Path-Planning for Power-Constrained UAV-Relays via SMDPs
Multiscale Adaptive Scheduling and Path-Planning for Power-Constrained UAV-Relays via SMDPs
复制标题
通过 SMDP 进行功率受限无人机中继的多尺度自适应调度和路径规划
DOI：
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复制
发表时间：
2022
期刊：
影响因子：
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通讯作者：
Nicolò Michelusi
中科院分区：
文献类型：
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作者：
Bharath Keshavamurthy;Nicolò Michelusi
我们描述了分散的旋翼无人机中继群的编排，增强了地面基站的覆盖范围和服务能力。我们的目标是在受到平均无人机功率限制的情况下，最大限度地减少泊松到达时处理地面用户传输请求所涉及的时间平均服务延迟。配备速率自适应以有效利用空对地随机性，我们首先通过半马尔可夫决策过程公式导出单个中继的最优控制策略，并针对无人机轨迹设计进行竞争群优化。因此，我们详细介绍了这种结构的多尺度分解：径向等待速度和最终位置的外部决策优化了预期的长期延迟功率权衡；因此，关于角等待速度、服务计划和无人机轨迹的内部决策贪婪地最小化了瞬时延迟功率成本。接下来，通过复制和共识驱动的命令与控制推广到无人机群，该策略嵌入了传播最大化和冲突解决启发法。我们证明，我们的框架在平均服务延迟和平均每架无人机功耗方面提供了卓越的性能：相对于静态无人机中继部署，数据有效负载传输速度快 11 倍，比 Deep-Q 网络解决方案快 2 倍；值得注意的是，我们的方案中的 1 个中继比联合连续凸逼近策略下的 3 个中继好 62%。
We describe the orchestration of a decentralized swarm of rotary-wing UAV-relays, augmenting the coverage and service capabilities of a terrestrial base station. Our goal is to minimize the time-average service latencies involved in handling transmission requests from ground users under Poisson arrivals, subject to an average UAV power constraint. Equipped with rate adaptation to efficiently leverage air-to-ground stochastics, we first derive the optimal control policy for a single relay via a semi-Markov decision process formulation, with competitive swarm optimization for UAV trajectory design. Accordingly, we detail a multiscale decomposition of this construction: outer decisions on radial wait velocities and end positions optimize the expected long-term delay-power trade-off; consequently, inner decisions on angular wait velocities, service schedules, and UAV trajectories greedily minimize the instantaneous delay-power costs. Next, generalizing to UAV swarms via replication and consensus-driven command-and-control, this policy is embedded with spread maximization and conflict resolution heuristics. We demonstrate that our framework offers superior performance vis-à-vis average service latencies and average per-UAV power consumption: $11\times$ faster data payload delivery relative to static UAV-relay deployments and $2\times$ faster than a deep-Q network solution; remarkably, 1 relay with our scheme outclasses 3 relays under a joint successive convex approximation policy by 62 %.
影响因子：
6.8
作者：
Abd-Elmagid, Mohamed A.;Dhillon, Harpreet S.
通讯作者：
Dhillon, Harpreet S.
MAESTRO-X：旋翼无人机中继群的分布式编排
DOI：
10.1109/tccn.2023.3248859
发表时间：
2023
影响因子：
8.6
作者：
Keshavamurthy, Bharath;Bliss, Matthew A.;Michelusi, Nicolò
通讯作者：
Michelusi, Nicolò