Abstract
Unmanned Aerial Vehicles, operating in environments with relatively few obstacles, offer high maneuverability and full three-dimensional mobility. This allows them to rapidly approach objects and perform a wide range of tasks often challenging for ground robots, making them ideal for exploration, inspection, aerial imaging, and everyday assistance. In this paper, we introduce AirStar, a UAV-centric embodied platform that turns a UAV into an intelligent aerial assistant: a large language model acts as the cognitive core for environmental understanding, contextual reasoning, and task planning. AirStar accepts natural interaction through voice commands and gestures, removing the need for a remote controller and significantly broadening its user base. It combines geospatial knowledge-driven long-distance navigation with contextual reasoning for fine-grained short-range control, resulting in an efficient and accurate vision-and-language navigation (VLN) this http URL, the system also offers built-in capabilities such as cross-modal question answering, intelligent filming, and target tracking. With a highly extensible framework, it supports seamless integration of new functionalities, paving the way toward a general-purpose, instruction-driven intelligent UAV agent. The supplementary PPT is available at \href{this https URL}{this https URL}.
Abstract (translated)
无人员驾驶飞行器（无人机）在障碍物相对较少的环境中运行时，具有很高的机动性和全方位三维移动能力。这使得它们能够迅速接近物体并执行地面机器人难以完成的一系列任务，如探索、检查、空中成像和日常辅助工作。本文介绍了AirStar，这是一个以无人机为中心的身体化平台，将无人机转化为智能空中小助手：大型语言模型充当环境理解、情境推理和任务规划的认知核心。AirStar通过语音命令和手势接受自然交互方式，从而免去了遥控器的需要，并大大拓宽了用户基础。它结合了基于地理空间知识的远程导航与情景推理驱动的精细近距离控制，实现了高效且准确的视觉-语言导航（VLN）。此外，该系统还内置了一些功能，如跨模式问答、智能拍摄和目标追踪。凭借高度可扩展的框架，它可以无缝集成新功能，为通用指令驱动型智能无人机代理的发展铺平道路。补充材料PPT可在\href{this https URL}{此链接}获取。
URL
https://arxiv.org/abs/2507.04430
