AeroDuo：面向无人机视觉与语言导航的空中双机系统
AeroDuo: Aerial Duo for UAV-based Vision and Language Navigation
AI总结：
本文提出双高度无人机协同视觉语言导航任务DuAl-VLN及AeroDuo框架，通过高空无人机负责环境推理、低空无人机负责精确导航，并构建HaL-13k数据集验证模型在新环境和陌生目标上的泛化能力。
摘要：
空中视觉与语言导航（VLN）是一项新兴任务，旨在使无人机（UAV）能够利用自然语言指令和视觉线索在户外环境中导航。然而，由于无人机轨迹较长且机动复杂性高，实现可靠的无人机VLN性能具有挑战性，通常需要人工干预或过于详细的指令。为了利用无人机高机动性所能提供的多粒度视角优势，同时保持可学习且可控的运动空间，我们提出了一项名为双高度无人机协同VLN（DuAl-VLN）的新任务。在该任务中，两架无人机在不同高度运行：高空无人机负责大范围环境推理，低空无人机负责精确导航。为了支持DuAl-VLN的训练与评估，我们构建了HaL-13k数据集，该数据集包含13,838条协同高低空无人机演示轨迹，每条轨迹均配有面向目标的语言指令。该数据集同时包含未见地图和未见物体验证集，以系统评估模型在新环境和陌生目标上的泛化能力。为了整合两架无人机的互补优势，我们提出了双无人机协同VLN框架AeroDuo，其中高空无人机集成多模态大语言模型（Pilot-LLM）进行目标推理，低空无人机采用轻量级多阶段策略进行导航和目标定位。两架无人机协同工作，仅交换最少的坐标信息以确保效率。
英文摘要：
Aerial Vision-and-Language Navigation (VLN) is an emerging task that enables Unmanned Aerial Vehicles (UAVs) to navigate outdoor environments using natural language instructions and visual cues. However, due to the extended trajectories and complex maneuverability of UAVs, achieving reliable UAV-VLN performance is challenging and often requires human intervention or overly detailed instructions. To harness the advantages of UAVs' high mobility, which could provide multi-grained perspectives, while maintaining a manageable motion space for learning, we introduce a novel task called Dual-Altitude UAV Collaborative VLN (DuAl-VLN). In this task, two UAVs operate at distinct altitudes: a high-altitude UAV responsible for broad environmental reasoning, and a low-altitude UAV tasked with precise navigation. To support the training and evaluation of the DuAl-VLN, we construct the HaL-13k, a dataset comprising 13,838 collaborative high-low UAV demonstration trajectories, each paired with target-oriented language instructions. This dataset includes both unseen maps and an unseen object validation set to systematically evaluate the model's generalization capabilities across novel environments and unfamiliar targets. To consolidate their complementary strengths, we propose a dual-UAV collaborative VLN framework, AeroDuo, where the high-altitude UAV integrates a multimodal large language model (Pilot-LLM) for target reasoning, while the low-altitude UAV employs a lightweight multi-stage policy for navigation and target grounding. The two UAVs work collaboratively and only exchange minimal coordinate information to ensure efficiency.
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。
