下达任务，执行蜂群：智能体增强的LLM推理在无人机网络中的应用
Say the Mission, Execute the Swarm: Agent-Enhanced LLM Reasoning in the Web-of-Drones
AI总结：
本文提出一种智能体增强的LLM框架，通过MCP网关和WoT标准化实现无人机蜂群的自然语言任务自主执行，实验表明通用LLM需接地支持才能可靠运行。
摘要：
大型语言模型（LLMs）正越来越多地被探索作为信息物理系统的高层推理引擎，然而，由于异构接口、有限的接地性以及长时间闭环执行的需求，它们在实际无人机蜂群管理中的应用仍然具有挑战性。本文提出了一种任务无关的、智能体增强的LLM框架，用于无人机蜂群控制，其中用户以自然语言表达任务目标，系统通过接地、实时的交互自主执行这些目标。所提出的架构将基于LLM的智能体核心与模型上下文协议（MCP）网关以及基于W3C物联网（WoT）标准的无人机网络抽象相结合。通过将无人机、传感器和服务暴露为标准化的WoT事物，该框架实现了结构化的基于工具交互、连续状态观察和安全执行，而无需依赖代码生成。我们使用基于ArduPilot的仿真，在四种蜂群任务和六种最先进的LLM上评估了该框架。结果表明，尽管具有强大的推理能力，当前通用的LLM在缺乏显式接地和执行支持的情况下，即使对于简单的蜂群任务，仍然难以实现可靠的执行。任务特定的规划工具和运行时护栏显著提高了鲁棒性，而仅凭令牌消耗并不能指示执行质量或可靠性。
英文摘要：
Large Language Models (LLMs) are increasingly explored as high-level reasoning engines for cyber-physical systems, yet their application to real-time UAV swarm management remains challenging due to heterogeneous interfaces, limited grounding, and the need for long-running closed-loop execution. This paper presents a mission-agnostic, agent-enhanced LLM framework for UAV swarm control, where users express mission objectives in natural language and the system autonomously executes them through grounded, real-time interactions. The proposed architecture combines an LLM-based Agent Core with a Model Context Protocol (MCP) gateway and a Web-of-Drones abstraction based on W3C Web of Things (WoT) standards. By exposing drones, sensors, and services as standardized WoT Things, the framework enables structured tool-based interaction, continuous state observation, and safe actuation without relying on code generation. We evaluate the framework using ArduPilot-based simulation across four swarm missions and six state-of-the-art LLMs. Results show that, despite strong reasoning abilities, current general-purpose LLMs still struggle to achieve reliable execution - even for simple swarm tasks - when operating without explicit grounding and execution support. Task-specific planning tools and runtime guardrails substantially improve robustness, while token consumption alone is not indicative of execution quality or reliability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
