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.

翻译：传统自主无人机搜索任务依赖忽略目标语义上下文的几何覆盖模式，在大规模环境中会导致显著的时间浪费。本文提出LMPath，一种利用语义生成无人机语言介导探索先验的流水线。给定基本地理围栏和感兴趣对象提示，LMPath利用生成式语言模型确定环境中应包含该对象的区域，并通过运行于卫星图像上的基础视觉模型分割构成探索先验的子区域。该先验可用于生成具有多种目标的无人机路径，如最小化定位感兴趣对象的预期时间、在有限行进距离内最大化发现对象的概率，或将搜索空间缩小至最可能包含对象的子区域。为验证其能力，我们使用LMPath生成多种无人机路径，并在真实无人机上将其部署于大规模环境。此外，我们通过仿真实验证明，基于LMPath生成的路径在执行搜索任务时优于传统路径规划方法。