智能体AI赋能自主、自组织与演进的无人机网络

中文摘要 AI 辅助

随着低空应用在应急响应、智能交通和自主操作等领域的扩展，这些应用对通信网络提出了能够提供灵活、有弹性和快速部署连接的需求。异构无人机网络是一种有前景的解决方案，因为它们能够动态地提供感知、接入、中继和回程功能。然而，大多数现有方法假设预定义的任务、用户分布的先前知识以及手动配置的基础设施，这使得它们难以适应动态且初始未知的环境。解决这一局限性需要从面向任务的无人机部署转向自主网络形成，其中无人机持续感知周围环境、推断不断演变的服务需求，并自组织网络资源。由大型语言模型（LLMs）赋能的智能体AI，通过整合跨异构信息源的闭环感知、推理、规划和执行，为这一转变提供了新的基础。与为单个网络任务设计的传统优化和学习方法不同，智能体AI能够协调这些能力以支持持续的网络级自主性。在本文中，我们探讨了智能体AI在低空环境中实现自主和自组织异构无人机网络的应用。我们的关键贡献是一种LLM辅助架构，其中基站托管的智能体进行全局网络推理并自主重新配置接入和回程基础设施。所提出的系统探索未知环境、发现用户，并按需部署无人机以提供接入并建立端到端的回程连接。一个案例研究展示了这种智能体AI驱动的方法如何将无人机从特定任务平台转变为持续演进的通信网络。

英文摘要

As low-altitude applications expand across emergency response, intelligent transportation, and autonomous operations, they demand communication networks that can deliver flexible, resilient, and rapidly deployable connectivity. Heterogeneous UAV networks are a promising solution, as they can dynamically provide sensing, access, relay, and backhaul functions. Yet, most existing approaches assume predefined missions, prior knowledge of user distributions, and manually configured infrastructure, making them ill suited to dynamic and initially unknown environments. Addressing this limitation requires a shift from mission-oriented UAV deployment to autonomous network formation, in which UAVs continuously perceive their surroundings, infer evolving service demands, and self-organize network resources. Agentic AI, empowered by large language models (LLMs), offers a new foundation for this shift by integrating closed-loop perception, reasoning, planning, and execution across heterogeneous information sources. Unlike conventional optimization and learning methods designed for individual networking tasks, agentic AI can coordinate these capabilities to support sustained, network-level autonomy. In this article, we explore agentic AI for autonomous and self-organizing heterogeneous UAV networks in low-altitude environments. Our key contribution is an LLM-assisted architecture in which a base-station-hosted agent conducts global network reasoning and autonomously reconfigures access and backhaul infrastructure. The proposed system explores unknown environments, discovers users, and deploys UAVs on demand to provide access and establish end-to-end backhaul connectivity. A case study illustrates how this agentic-AI-driven approach can transform UAVs from task-specific platforms into a continuously evolving communication network.

发表机构

• College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息与电子工程学院）

机构由 AI 辅助整理，请以论文原文为准。