This letter tackles the challenge of autonomous target search using unmanned aerial vehicles (UAVs) in complex unknown environments. To fill the gap in systematic approaches for this task, we introduce Star-Searcher, an aerial system featuring specialized sensor suites, mapping, and planning modules to optimize searching. Path planning challenges due to increased inspection requirements are addressed through a hierarchical planner with a visibility-based viewpoint clustering method. This simplifies planning by breaking it into global and local sub-problems, ensuring efficient global and local path coverage in real-time. Furthermore, our global path planning employs a history-aware mechanism to reduce motion inconsistency from frequent map changes, significantly enhancing search efficiency. We conduct comparisons with state-of-the-art methods in both simulation and the real world, demonstrating shorter flight paths, reduced time, and higher target search completeness. Our approach will be open-sourced for community benefit. ¹

中文翻译：

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寻星者：复杂未知环境下自主目标搜索的完整高效航空系统

这封信解决了在复杂的未知环境中使用无人机（UAV）进行自主目标搜索的挑战。为了填补这项任务系统方法的空白，我们推出了 Star-Searcher，这是一种航空系统，具有专门的传感器套件、测绘和规划模块来优化搜索。通过具有基于可见性的视点聚类方法的分层规划器解决了由于检查要求增加而导致的路径规划挑战。这通过将规划分解为全局和局部子问题来简化规划，确保实时有效的全局和局部路径覆盖。此外，我们的全局路径规划采用历史感知机制来减少频繁地图更改导致的运动不一致，从而显着提高搜索效率。我们在模拟和现实世界中与最先进的方法进行比较，展示了更短的飞行路径、更少的时间和更高的目标搜索完整性。我们的方法将开源以造福社区。 ¹