无人机搜索终于有了语义理解能力——LMPath 用语言模型和视觉模型替代纯几何覆盖，做搜救、巡检、环境监测的团队可以直接参考，实测效率提升明显。
传统无人机自主搜索依赖几何覆盖模式，忽略目标语义，在大规模环境中效率低下。LMPath 提出一种新流程：给定地理围栏和目标描述，先用生成式语言模型判断目标可能出现的区域，再用视觉基础模型对卫星图像分割，形成语义探索先验。基于该先验，可生成多种无人机路径，如最小化预期搜索时间、在有限航程内最大化发现概率，或缩小搜索范围到最可能区域。真实无人机和仿真实验表明，LMPath 生成的路径在搜索任务中显著优于传统规划方法。
LMPath: Language-Mediated Priors and Path Generation for Aerial Exploration
Traditional autonomous UAV search missions rely on geometric coverage patterns that ignore the semantic context of the target, leading to significant time waste in large-scale environments. In this paper we present LMPath, a pipeline for generating language-mediated exploration priors for Unmanned Aerial Vehicle (UAV) search missions that leverages semantics. Given a basic geofence and an object of interest prompt, LMPath uses generative language models to determine what regions of the environment should contain that object and a foundation vision model ran over satellite imagery to segment sub-regions that form the exploration prior. This prior can then be used to generate UAV paths with various objectives, such as minimizing the expected time to locate the object of interest, maximizing the probability that the object is found given a limited travel distance, or narrowing down the search space to sub-regions that are most likely to contain the object. To demonstrate it's capabilities, we used LMPath to generate various UAV paths and ran them using a real UAV over large-scale environments. We also ran simulations to demonstrate how paths generated using LMPath outperform traditional path planning approaches for search missions.