标题： SkyVLN：城市环境中无人机的视觉-语言导航和NMPC控制 显示英文标题

标题： SkyVLN: Vision-and-Language Navigation and NMPC Control for UAVs in Urban Environments

摘要： 无人机（UAV）已成为各个领域中多功能的工具，这是由于它们的移动性和适应性。 本文介绍了SkyVLN，这是一种将视觉与语言导航（VLN）与非线性模型预测控制（NMPC）相结合的新框架，以提高无人机在复杂城市环境中的自主性。 与传统的导航方法不同，SkyVLN利用大语言模型（LLMs）来解释自然语言指令和视觉观察，使无人机能够在动态三维空间中更准确和稳健地导航。 我们提出了一种多模态导航代理，配备了细粒度的空间描述器和历史路径记忆机制。 这些组件使无人机能够消除空间上下文的歧义，处理模糊的指令，并在必要时回溯。 该框架还集成了一个NMPC模块，用于动态避障，确保精确的轨迹跟踪和碰撞预防。 为了验证我们的方法，我们使用AirSim开发了一个高保真度的3D城市仿真环境，包含逼真的图像和动态城市元素。 大量实验表明，SkyVLN显著提高了导航成功率和效率，特别是在新出现和未见过的环境中。 显示英文摘要

摘要： Unmanned Aerial Vehicles (UAVs) have emerged as versatile tools across various sectors, driven by their mobility and adaptability. This paper introduces SkyVLN, a novel framework integrating vision-and-language navigation (VLN) with Nonlinear Model Predictive Control (NMPC) to enhance UAV autonomy in complex urban environments. Unlike traditional navigation methods, SkyVLN leverages Large Language Models (LLMs) to interpret natural language instructions and visual observations, enabling UAVs to navigate through dynamic 3D spaces with improved accuracy and robustness. We present a multimodal navigation agent equipped with a fine-grained spatial verbalizer and a history path memory mechanism. These components allow the UAV to disambiguate spatial contexts, handle ambiguous instructions, and backtrack when necessary. The framework also incorporates an NMPC module for dynamic obstacle avoidance, ensuring precise trajectory tracking and collision prevention. To validate our approach, we developed a high-fidelity 3D urban simulation environment using AirSim, featuring realistic imagery and dynamic urban elements. Extensive experiments demonstrate that SkyVLN significantly improves navigation success rates and efficiency, particularly in new and unseen environments.