Star-Searcher: A Complete and Efficient Aerial System for Autonomous Target Search in Complex Unknown Environments
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866911806631444480 |
|---|---|
| author | Luo, Yiming Zhuang, Zixuan Pan, Neng Feng, Chen Shen, Shaojie Gao, Fei Cheng, Hui Zhou, Boyu |
| author_facet | Luo, Yiming Zhuang, Zixuan Pan, Neng Feng, Chen Shen, Shaojie Gao, Fei Cheng, Hui Zhou, Boyu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_16348 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Star-Searcher: A Complete and Efficient Aerial System for Autonomous Target Search in Complex Unknown Environments Luo, Yiming Zhuang, Zixuan Pan, Neng Feng, Chen Shen, Shaojie Gao, Fei Cheng, Hui Zhou, Boyu Robotics 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. |
| title | Star-Searcher: A Complete and Efficient Aerial System for Autonomous Target Search in Complex Unknown Environments |
| topic | Robotics |
| url | https://arxiv.org/abs/2402.16348 |