用分层大模型实现无人机在动态空网中的高效协同导航
Intelligent Multi-UAV Navigation in ITNTNs: A Hierarchical LLM Approach
- 云端大模型统筹全局，边缘小模型处理局部任务
- 碰撞率下降，系统总吞吐量显著提升
- 适合高密度无人机空域管理与智能交通系统
高速无人飞行器在三维空中高速公路中的部署，要求对物理飞行运动学和多层级网络切换进行稳健协调。尽管深度强化学习（DRL）能实现快速战术控制，却缺乏应对动态集成陆地与非地面网络（ITNTNs）的零样本战略推理能力。相反，大语言模型（LLMs）擅长语义推理，但推理延迟高，难以用于实时气动控制。为此，我们提出一种新型分层式大模型驱动控制框架：部署于高空平台站（HAPS）的大型云模型负责慢时尺度的全局负载均衡，而各无人机上的轻量级边缘-LLM将本地观测转化为战术子目标。这些子目标指导快速时尺度的物理DRL控制器执行无碰撞、支持切换的轨迹。仿真结果表明，该代理架构相比现有基线显著降低碰撞率并提升系统总吞吐量。
原文摘要 · Abstract (English)
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.
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