Intelligent Multi-UAV Navigation in ITNTNs: A Hierarchical LLM Approach
A hierarchical LLM framework combining cloud-based and edge LLMs with DRL for UAV navigation in ITNTNs, reducing collisions and improving throughput.
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[Submitted on 21 Jul 2026]
Title:Intelligent Multi-UAV Navigation in ITNTNs: A Hierarchical LLM Approach
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Abstract: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.
Comments: This paper has been accepted by IEEE GLOBECOM 2026
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI); Systems and Control (eess.SY)
Cite as: arXiv:2607.18604 [cs.RO]
(or arXiv:2607.18604v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.18604
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Zijiang Yan [view email] [v1] Tue, 21 Jul 2026 00:34:42 UTC (1,579 KB)
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