# Star-Searcher: A Complete and Efficient Aerial System for Autonomous Target Search in Complex Unknown Environments

arXiv: https://arxiv.org/abs/2402.16348 · HTML: https://mlsanity.com/paper/2402.16348/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2024.

## Built on by (most cited first, 3 of 3)

- [AirHunt: Bridging VLM Semantics and Continuous Planning for Efficient Aerial Object Navigation](https://mlsanity.com/paper/2601.12742/) (2026)
- [Semantic Exploration and Dense Mapping of Complex Environments using Ground Robot with Panoramic LiDAR-Camera Fusion](https://mlsanity.com/paper/2505.22880/) (2025)
- [SR-Nav: Spatial Relationships Matter for Zero-shot Object Goal Navigation](https://mlsanity.com/paper/2603.18443/) (2026)

## Abstract (arXiv)

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.