AAAI 2025technical0 citations
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
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.
BibTeX
@article{Xu_Xie_Ding_Zeng_Wang_Zhang_2025, title={UACOF: A USV-AUV Collaboration Framework for Underwater Tasks Under Extreme Sea Conditions (Student Abstract)}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/35317}, DOI={10.1609/aaai.v39i28.35317}, abstractNote={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.}, number={28}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Xu, Jingzehua and Xie, Guanwen and Ding, Yimian and Zeng, Yongming and Wang, Haoyu and Zhang, Shuai}, year={2025}, month={Apr.}, pages={29538-29540} }