LLM-Land：面向情境感知无人机降落的大语言模型
LLM-Land: Large Language Models for Context-Aware Drone Landing
AI总结：
本文提出 LLM-Land 混合框架，利用 VLE、轻量级 LLM 与 RAG 推断场景安全缓冲，并结合 MPC 实时重规划，提升无人机在动态杂乱环境中的安全精准降落能力。
摘要：
自主降落对于部署在紧急配送、灾后响应及其他大规模任务中的无人机至关重要。通过实现充电平台上的自动对接，它可促进持续运行并显著延长任务续航时间。然而，由于语义感知能力有限且依赖固定、对情境不敏感的安全裕度，传统方法在动态、非结构化环境中往往表现不足。为克服这些局限，我们提出一种将大语言模型（LLM）与模型预测控制（MPC）相结合的混合框架。该方法首先使用视觉-语言编码器（VLE，例如 BLIP），将实时图像转换为简洁的文本场景描述。这些描述由配备检索增强生成（RAG）的轻量级 LLM（例如 Qwen 2.5 1.5B 或 LLaMA 3.2 1B）处理，用于对场景元素进行分类并推断情境感知安全缓冲，例如针对行人的 3 米和针对车辆的 5 米。随后，所得语义标志和不安全区域被输入 MPC 模块，从而实现实时轨迹重规划，在避免碰撞的同时保持高降落精度。我们在 ROS-Gazebo 仿真器中验证了该框架，其表现始终优于传统的基于视觉的 MPC 基线。结果表明，该框架显著减少了与动态障碍物的险撞事件，同时在杂乱环境中保持了精准降落。
英文摘要：
Autonomous landing is essential for drones deployed in emergency deliveries, post-disaster response, and other large-scale missions. By enabling self-docking on charging platforms, it facilitates continuous operation and significantly extends mission endurance. However, traditional approaches often fall short in dynamic, unstructured environments due to limited semantic awareness and reliance on fixed, context-insensitive safety margins. To address these limitations, we propose a hybrid framework that integrates large language model (LLMs) with model predictive control (MPC). Our approach begins with a vision-language encoder (VLE) (e.g., BLIP), which transforms real-time images into concise textual scene descriptions. These descriptions are processed by a lightweight LLM (e.g., Qwen 2.5 1.5B or LLaMA 3.2 1B) equipped with retrieval-augmented generation (RAG) to classify scene elements and infer context-aware safety buffers, such as 3 meters for pedestrians and 5 meters for vehicles. The resulting semantic flags and unsafe regions are then fed into an MPC module, enabling real-time trajectory replanning that avoids collisions while maintaining high landing precision. We validate our framework in the ROS-Gazebo simulator, where it consistently outperforms conventional vision-based MPC baselines. Our results show a significant reduction in near-miss incidents with dynamic obstacles, while preserving accurate landings in cluttered environments.
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。
