This paper 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 at https://github.com/SYSU-STAR/STAR-Searcher.

翻译：本文解决了在复杂未知环境下使用无人机进行自主目标搜索的挑战。针对该任务缺乏系统性方法的不足，我们提出了Star-Searcher——一种配备专用传感器套件、建图与规划模块的空中系统，以优化搜索性能。针对增强型巡检需求带来的路径规划难题，我们采用基于可视性的视点聚类方法设计了分层规划器。该方法通过将规划问题分解为全局与局部子问题，简化了规划过程，确保了实时高效的全局与局部路径覆盖。此外，全局路径规划采用历史感知机制，减少因地图频繁更新导致的运动不一致性，显著提升搜索效率。我们在仿真和真实环境中与最新方法进行了对比，证明了本方法具有更短的飞行路径、更少的时间消耗以及更高的目标搜索完整度。本方法将开源以惠及社区，代码见https://github.com/SYSU-STAR/STAR-Searcher。