智能无人机：基于大语言模型的自主性与集成工具调用和认知推理
Agentic UAVs: LLM-Driven Autonomy with Integrated Tool-Calling and Cognitive Reasoning
AI总结：
本文提出Agentic UAVs框架，利用大语言模型实现无人机的高自主性与外部系统集成，通过模拟实验验证其在搜索救援任务中的有效性。
摘要：
无人机（UAVs）越来越多地用于国防、监视和灾害响应，但大多数系统仍仅在SAE 2至3级自主性水平上运行。它们对基于规则的控制和窄AI的依赖限制了在动态和不确定任务中的适应性。当前的无人机架构缺乏上下文感知推理、自主决策和与外部系统的集成。重要的是，目前没有任何系统利用大语言模型（LLM）代理通过工具调用实现实时知识访问。本文介绍了Agentic UAVs框架，该框架由感知、推理、行动、集成和学习五个层次组成。该框架通过LLM驱动的推理、数据库查询和与第三方系统的交互来增强无人机的自主性。一个基于ROS 2和Gazebo构建的原型结合了YOLOv11用于目标检测，GPT-4用于推理，以及本地部署的Gemma 3模型。在模拟的搜索和救援场景中，智能无人机实现了更高的检测置信度（0.79对比0.72）、改进的人检测率（91%对比75%）以及正确的行动推荐显著增加（92%对比4.5%）。这些结果表明，适度的计算开销可以实现显著更高的自主性和系统级集成。
英文摘要：
Unmanned Aerial Vehicles (UAVs) are increasingly used in defense, surveillance, and disaster response, yet most systems still operate at SAE Level 2 to 3 autonomy. Their dependence on rule-based control and narrow AI limits adaptability in dynamic and uncertain missions. Current UAV architectures lack context-aware reasoning, autonomous decision-making, and integration with external systems. Importantly, none make use of Large Language Model (LLM) agents with tool-calling for real-time knowledge access. This paper introduces the Agentic UAVs framework, a five-layer architecture consisting of Perception, Reasoning, Action, Integration, and Learning. The framework enhances UAV autonomy through LLM-driven reasoning, database querying, and interaction with third-party systems. A prototype built with ROS 2 and Gazebo combines YOLOv11 for object detection with GPT-4 for reasoning and a locally deployed Gemma 3 model. In simulated search-and-rescue scenarios, agentic UAVs achieved higher detection confidence (0.79 compared to 0.72), improved person detection rates (91% compared to 75%), and a major increase in correct action recommendations (92% compared to 4.5%). These results show that modest computational overhead can enable significantly higher levels of autonomy and system-level integration.
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。
