基于大语言模型的空间语义推理用于高效无人机搜索任务
Spatial-Semantic Reasoning using Large Language Models for Efficient UAV Search Operations
- University of Zagreb（萨格勒布大学）
- Faculty of Electrical Engineering and Computing（电气工程与计算机学院）
- LARICS (Laboratory for Robotics and Intelligent Control Systems)（拉里克机器人与智能控制系统实验室）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究提出基于大语言模型的无人机实时语义导航框架，结合多技术实现高效目标导航，经仿真与真实实验验证可缩短任务时长且保持高搜索准确率。
AI中文摘要：
我们提出了一种面向无人机（UAV）的实时语义导航框架，旨在提升目标目标导航（ObjectNav）任务的时间效率。该方法的核心是大语言模型（LLM），它能解析用户提供的自然语言指令，并对检测到的物体和空间上下文进行语义推理，以优先选择高概率搜索区域。该系统结合了实时目标检测、3D空间映射和多项式样条插值，用于生成平滑且可行的无人机轨迹规划。与依赖离线推理或模拟器约束动作空间的现有方法不同，我们的框架可实时运行，并根据新观测结果持续更新语义相关性。在仿真和真实环境中的实验表明，该框架缩短了任务时长，同时保持了较高的搜索准确率，凸显了LLM引导的推理在基于无人机的高效ObjectNav中的有效性。
英文摘要：
We present a real-time semantic navigation framework for Unmanned Aerial Vehicles (UAVs) focused on improving time efficiency in the Object Goal Navigation (ObjectNav) task. Central to our approach is a Large Language Model (LLM) that interprets user-provided natural language instructions and performs semantic reasoning over detected objects and spatial context to prioritize high-probability search regions. The system combines real-time object detection, 3D spatial mapping, and polynomial spline interpolation for smooth and feasible UAV trajectory planning. Unlike prior methods that rely on offline reasoning or simulator-constrained action spaces, our framework can operate in real time, continuously updating semantic relevance based on new observations. Experiments in both simulated and real-world settings demonstrate reductions in mission duration while maintaining high search accuracy, underscoring the effectiveness of LLM-guided reasoning for time- efficient UAV-based ObjectNav.
