智能多无人机在综合陆地与非陆地网络中的导航：一种分层大语言模型方法
Intelligent Multi-UAV Navigation in ITNTNs: A Hierarchical LLM Approach
浏览论文内容
中文总结 AI 辅助
针对高速无人机在三维空中高速公路部署中面临的协调问题，提出分层大语言模型驱动控制框架，利用云端大语言模型管理全局负载平衡，边缘大语言模型转化局部观测为子目标，引导深度强化学习控制器执行轨迹，降低碰撞率并提高系统吞吐量。
中文摘要 AI 辅助
在三维空中高速公路中部署高速无人机需要对物理飞行运动学和多层网络切换进行稳健协调。深度强化学习虽能提供快速战术控制，但缺乏快速适应动态综合陆地与非陆地网络所需的零样本战略推理。大语言模型擅长语义推理，但推理延迟高，不适用于实时空气动力学控制。为弥合这一差距，我们提出一种新颖的分层大语言模型驱动控制框架。基于云的大型大语言模型管理慢时间尺度全局负载平衡，而单个无人机上的轻量级边缘大语言模型将局部观测转化为战术子目标，引导快速时间尺度的物理深度强化学习控制器执行无碰撞、可感知切换的轨迹。模拟结果表明，与现有基线相比，我们的智能体架构显著降低了碰撞率并提高了系统总吞吐量。
英文摘要
The deployment of high-speed Uncrewed Aerial Vehicles (UAVs) in 3D aerial highways necessitates robust coordination of physical flight kinematics and multi-tier network handovers. While Deep Reinforcement Learning (DRL) offers rapid tactical control, it lacks the zero-shot strategic reasoning required to quickly adapt to dynamic Integrated Terrestrial and Non-Terrestrial Networks (ITNTNs). Conversely, Large Language Models (LLMs) excel at semantic reasoning but suffer from high inference latency, rendering them unsuitable for real-time aerodynamic control. To bridge this gap, we propose a novel Hierarchical LLM-driven control framework. A massive cloud-based LLM deployed on a High-Altitude Platform Station (HAPS) manages slow-timescale global load balancing, while lightweight edge-LLMs on individual UAVs translate local observations into tactical sub-goals. These sub-goals guide a fast-timescale physical DRL controller to execute collision-free, handover-aware trajectories. Simulation results demonstrate that our agentic architecture significantly reduces collision rates and improves aggregate system throughput compared to existing baselines.
