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

Abstract

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.

Aceepted to IEEE RA-L. Code: https://github.com/SYSU-STAR/STAR-Searcher. Video: https://www.youtube.com/watch?v=08ll_oo_DtU

References in corpus (4)

• An Efficient Sampling-based Method for Online Informative Path Planning in Unknown Environments
• Autonomous UAV Exploration of Dynamic Environments via Incremental Sampling and Probabilistic Roadmap
• Finding Things in the Unknown: Semantic Object-Centric Exploration with an MAV
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Cited by in corpus (1)

• Semantic Exploration and Dense Mapping of Complex Environments using Ground Robot with Panoramic LiDAR-Camera Fusion