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English(EN) Intelligent Multi-UAV Navigation in ITNTNs: A Hierarchical LLM Approach

LLM-DRL混合导航复杂网络中的UAV

研究人员开发了一种用于在复杂集成陆地和非陆地网络(ITNTN)中导航的无人机(UAV)的新分层控制框架。该系统结合了大型语言模型(LLM)的战略推理和深度强化学习(DRL)的快速控制。基于云的LLM负责全局网络平衡,而部署在各个UAV上的小型边缘LLM则将观测转化为DRL控制器的战术目标。模拟结果表明,该方法显著减少了碰撞并提高了系统吞吐量。 AI

影响 这种混合LLM-DRL方法可以为自主系统在复杂、动态环境中提供更复杂、更高效的导航能力。

排序理由 这是一篇详细介绍新颖技术方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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LLM-DRL混合导航复杂网络中的UAV

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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Zijiang Yan, Hao Zhou, Wael Jaafar, Jianhua Pei, Ping Wang, Halim Yanikomeroglu, Hina Tabassum ·

    ITNTN中的智能多无人机导航:一种分层LLM方法

    arXiv:2607.18604v1 Announce Type: cross 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 rap…