通用大型语言模型——无人机指挥与控制接口
An LLM-Agnostic, MAVLink-Based Drone Command and Control Interface and Agentic Harness Using the Model Context Protocol
AI总结：
本文提出了一种通用的无人机控制接口，通过MCP协议实现LLM与无人机的无缝集成，提供实时导航和飞行控制能力。
摘要：
人工智能（AI）在无人机控制中的应用可以对无人机能力产生变革性影响，尤其是在将现实世界信息与无人机感知、指令和控制相结合时，成为日益发展的物理AI领域的一部分。大型语言模型（LLMs）如果在通用知识上大规模训练，可以具有优势，特别是当训练数据包含如整个星球的详细地图地理拓扑信息，以及能够访问实时态势数据如天气的能力。然而，无人机与LLMs之间的接口仍存在挑战，每个应用都需要繁琐、劳动密集的努力才能将训练好的LLM知识连接到无人机指令和控制。在这里，我们解决了这个问题，采用一种与LLM无关和无人机无关的接口策略，提供第一个通用、多功能、全面且易于使用的无人机控制接口。我们使用新的模型上下文协议（MCP）标准，这是一个开放标准，为AI系统提供访问外部数据、工具和服务的通用方法。我们开发并部署了一个基于云的Linux机器，托管一个支持Mavlink协议的MCP服务器，这是一种被几乎所有无人机使用的通用无人机控制语言，包括Ardupilot和PX4框架。我们展示了对真实无人驾驶航空器的飞行控制。在进一步测试中，我们展示了在模拟无人机中广泛飞行规划和控制能力，与Google Maps MCP服务器集成，以获得最新的实时导航信息。这展示了将LLMs与无人机指令和控制相结合的通用方法，一种利用并利用现代AI行业几乎所有技术的范式，通过易于使用的界面将自然语言转换为无人机控制。
英文摘要：
Artificial intelligence integrated with drone command and control (physical AI) offers a route to autonomy through large language models (LLMs), yet a unified LLM-to-drone interface has been missing. We present an LLM-agnostic command-and-control interface and agentic harness joining the Model Context Protocol (MCP) to MAVLink, the near-universal drone command-and-telemetry standard: "DroneServer" gives any MCP-capable LLM command, telemetry, mission, and safety functions over ArduPilot and PX4. Its 98 tools cover 223 of 238 client-side methods of MavSDK, MAVLink's library; 61 of the 98 were exercised in software-in-the-loop. We treat the LLM as an untrusted commander: zero public ports (externally verified); every command validated server-side (tiers, confirmation handshakes, independent geofence, audit log), exercised by an adversarial safety suite. Server-side mission state resolves the "fire-and-forget" mismatch: a scripted client's 37.8-minute mission survived a 4-minute disconnection. Over 1,000 simulated flights ran a ten-mission LLM-UAV control benchmark; eight of eleven models complete 90.0-100% of its six flying missions, and no aircraft left the permitted zone in 110 geofence-violation trials. On real hardware, the unmodified server commanded three quadcopters, GPS-guided and GPS-denied; five providers' models flew an unseen mission, ten of ten. The tests and datasets include: repeated trials, standardized evaluation tasks, success/failure statistics, latency analysis, uncertainty treatment, comparative benchmarking across models/platforms, prompt sets, mission complexity, tool-call traces, safety-intervention logs, and safety-performance assessment. The architecture is a scaffold for a higher-level drone-LLM operating system where plain-language commands realize complex missions, and a path to agent swarms commanding drone swarms under one security umbrella.
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。
