UACOF: A USV-AUV Collaboration Framework for Underwater Tasks Under Extreme Sea Conditions (Student Abstract)

Jingzehua Xu, Guanwen Xie, Yimian Ding, Yongming Zeng, Haoyu Wang, Shuai Zhang

AAAI 2025 pp. 29538-29540

doi:10.1609/AAAI.V39I28.35317 /aaai/2025/xu2025aaai-uacof/

Abstract

Ocean exploration requires effective collaboration between the unmanned surface vehicle (USV) and autonomous underwater vehicles (AUVs). We propose UACOF, a USV-AUV collaboration framework that enhances multi-AUV performance under extreme sea conditions. The framework includes high-precision multi-AUV location via USV path planning with Fisher information matrix optimization and reinforcement learning training for cooperative tasks. Experimental results show UACOF's superior feasibility, performance, coordination and robustness in extreme conditions.

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Cite

Text

Xu et al. "UACOF: A USV-AUV Collaboration Framework for Underwater Tasks Under Extreme Sea Conditions (Student Abstract)." AAAI Conference on Artificial Intelligence, 2025. doi:10.1609/AAAI.V39I28.35317

Markdown

[Xu et al. "UACOF: A USV-AUV Collaboration Framework for Underwater Tasks Under Extreme Sea Conditions (Student Abstract)." AAAI Conference on Artificial Intelligence, 2025.](https://mlanthology.org/aaai/2025/xu2025aaai-uacof/) doi:10.1609/AAAI.V39I28.35317

BibTeX

@inproceedings{xu2025aaai-uacof,
  title     = {{UACOF: A USV-AUV Collaboration Framework for Underwater Tasks Under Extreme Sea Conditions (Student Abstract)}},
  author    = {Xu, Jingzehua and Xie, Guanwen and Ding, Yimian and Zeng, Yongming and Wang, Haoyu and Zhang, Shuai},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2025},
  pages     = {29538-29540},
  doi       = {10.1609/AAAI.V39I28.35317},
  url       = {https://mlanthology.org/aaai/2025/xu2025aaai-uacof/}
}