用无人机和地面车协同定位与路径规划，提升灾后通信质量。
Joint UAV-UGV Positioning and Trajectory Planning via Meta A3C for Reliable Emergency Communications
- 结合道路图建模地面车移动，约束合理路径
- 新方法使吞吐量高13.1%，执行快49%且满足服务质量
- 适合应急通信系统设计者快速适配复杂环境
无人飞行器（UAV）与无人地面车（UGV）的协同部署已被证明是灾害区域建立通信的有效方法。然而，在尽量减少无人机数量的同时保障良好服务质量（QoS），仍需对无人机和地面车进行最优定位与轨迹规划。本文提出一种基于无人机-地面车协同的定位与轨迹规划框架，以确保地面用户的服务质量。为建模地面车的移动性，引入道路图，引导其沿有效路段移动并遵守道路网络约束。针对总速率优化问题，将问题重新表述为马尔可夫决策过程（MDP），并提出一种融合元学习的异步优势演员-评论家（Meta-A3C）算法，实现对新环境和动态条件的快速适应。数值结果表明，所提出的Meta-A3C方法优于A3C和DDPG，吞吐量提升13.1%，执行速度加快49%，同时满足QoS要求。
原文摘要 · Abstract (English)
Joint deployment of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) has been shown to be an effective method to establish communications in areas affected by disasters. However, ensuring good Quality of Services (QoS) while using as few UAVs as possible also requires optimal positioning and trajectory planning for UAVs and UGVs. This paper proposes a joint UAV-UGV-based positioning and trajectory planning framework for UAVs and UGVs deployment that guarantees optimal QoS for ground users. To model the UGVs' mobility, we introduce a road graph, which directs their movement along valid road segments and adheres to the road network constraints. To solve the sum rate optimization problem, we reformulate the problem as a Markov Decision Process (MDP) and propose a novel asynchronous Advantage Actor Critic (A3C) incorporated with meta-learning for rapid adaptation to new environments and dynamic conditions. Numerical results demonstrate that our proposed Meta-A3C approach outperforms A3C and DDPG, delivering 13.1\% higher throughput and 49\% faster execution while meeting the QoS requirements.
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