Star-Searcher: 複雑未知環境における自律的目標探索のための完全かつ効率的な空中システム
Star-Searcher: A Complete and Efficient Aerial System for Autonomous Target Search in Complex Unknown Environments
複雑な未知環境でUAVが自律的に目標を探索するためのシステムを提案し、視認性に基づく視点クラスタリングと履歴を考慮した階層的経路計画により、探索効率と完全性を向上させた。
著者: Yiming Luo, Zixuan Zhuang, Neng Pan, Chen Feng, Shaojie Shen, Fei Gao, Hui Cheng, Boyu Zhou
分類: cs.RO
原文アブストラクト
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.
