USV-AUV Collaboration Framework for Underwater Tasks under Extreme Sea Conditions
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910780231778304 |
|---|---|
| author | Xu, Jingzehua Xie, Guanwen Wang, Xinqi Ding, Yimian Zhang, Shuai |
| author_facet | Xu, Jingzehua Xie, Guanwen Wang, Xinqi Ding, Yimian Zhang, Shuai |
| contents | Autonomous underwater vehicles (AUVs) are valuable for ocean exploration due to their flexibility and ability to carry communication and detection units. Nevertheless, AUVs alone often face challenges in harsh and extreme sea conditions. This study introduces a unmanned surface vehicle (USV)-AUV collaboration framework, which includes high-precision multi-AUV positioning using USV path planning via Fisher information matrix optimization and reinforcement learning for multi-AUV cooperative tasks. Applied to a multi-AUV underwater data collection task scenario, extensive simulations validate the framework's feasibility and superior performance, highlighting exceptional coordination and robustness under extreme sea conditions. To accelerate relevant research in this field, we have made the simulation code (demo version) available as open-source. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_02444 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | USV-AUV Collaboration Framework for Underwater Tasks under Extreme Sea Conditions Xu, Jingzehua Xie, Guanwen Wang, Xinqi Ding, Yimian Zhang, Shuai Robotics Systems and Control Autonomous underwater vehicles (AUVs) are valuable for ocean exploration due to their flexibility and ability to carry communication and detection units. Nevertheless, AUVs alone often face challenges in harsh and extreme sea conditions. This study introduces a unmanned surface vehicle (USV)-AUV collaboration framework, which includes high-precision multi-AUV positioning using USV path planning via Fisher information matrix optimization and reinforcement learning for multi-AUV cooperative tasks. Applied to a multi-AUV underwater data collection task scenario, extensive simulations validate the framework's feasibility and superior performance, highlighting exceptional coordination and robustness under extreme sea conditions. To accelerate relevant research in this field, we have made the simulation code (demo version) available as open-source. |
| title | USV-AUV Collaboration Framework for Underwater Tasks under Extreme Sea Conditions |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2409.02444 |