UACOF: A USV-AUV Collaboration Framework for Underwater Tasks under Extreme Sea Conditions

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

doi:10.1609/aaai.v39i28.35317

UACOF: A USV-AUV Collaboration Framework for Underwater Tasks under Extreme Sea Conditions

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

Data Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

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.

Original language	English (US)
Title of host publication	Special Track on AI Alignment
Editors	Toby Walsh, Julie Shah, Zico Kolter
Publisher	Association for the Advancement of Artificial Intelligence
Pages	29538-29540
Number of pages	3
Edition	28
ISBN (Electronic)	157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978
DOIs	https://doi.org/10.1609/aaai.v39i28.35317
State	Published - Apr 11 2025
Event	39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025 - Philadelphia, United States Duration: Feb 25 2025 → Mar 4 2025

Publication series

Name	Proceedings of the AAAI Conference on Artificial Intelligence
Number	28
Volume	39
ISSN (Print)	2159-5399
ISSN (Electronic)	2374-3468

Conference

Conference	39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025
Country/Territory	United States
City	Philadelphia
Period	2/25/25 → 3/4/25

All Science Journal Classification (ASJC) codes

Artificial Intelligence

Access to Document

10.1609/aaai.v39i28.35317

Cite this

Xu, J., Xie, G., Ding, Y., Zeng, Y., Wang, H., & Zhang, S. (2025). UACOF: A USV-AUV Collaboration Framework for Underwater Tasks under Extreme Sea Conditions. In T. Walsh, J. Shah, & Z. Kolter (Eds.), Special Track on AI Alignment (28 ed., pp. 29538-29540). (Proceedings of the AAAI Conference on Artificial Intelligence; Vol. 39, No. 28). Association for the Advancement of Artificial Intelligence. https://doi.org/10.1609/aaai.v39i28.35317

Xu, Jingzehua ; Xie, Guanwen ; Ding, Yimian et al. / UACOF : A USV-AUV Collaboration Framework for Underwater Tasks under Extreme Sea Conditions. Special Track on AI Alignment. editor / Toby Walsh ; Julie Shah ; Zico Kolter. 28. ed. Association for the Advancement of Artificial Intelligence, 2025. pp. 29538-29540 (Proceedings of the AAAI Conference on Artificial Intelligence; 28).

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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.",

author = "Jingzehua Xu and Guanwen Xie and Yimian Ding and Yongming Zeng and Haoyu Wang and Shuai Zhang",

note = "Publisher Copyright: Copyright {\textcopyright} 2025, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; 39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025 ; Conference date: 25-02-2025 Through 04-03-2025",

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doi = "10.1609/aaai.v39i28.35317",

language = "English (US)",

series = "Proceedings of the AAAI Conference on Artificial Intelligence",

publisher = "Association for the Advancement of Artificial Intelligence",

number = "28",

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Xu, J, Xie, G, Ding, Y, Zeng, Y, Wang, H & Zhang, S 2025, UACOF: A USV-AUV Collaboration Framework for Underwater Tasks under Extreme Sea Conditions. in T Walsh, J Shah & Z Kolter (eds), Special Track on AI Alignment. 28 edn, Proceedings of the AAAI Conference on Artificial Intelligence, no. 28, vol. 39, Association for the Advancement of Artificial Intelligence, pp. 29538-29540, 39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025, Philadelphia, United States, 2/25/25. https://doi.org/10.1609/aaai.v39i28.35317

UACOF: A USV-AUV Collaboration Framework for Underwater Tasks under Extreme Sea Conditions. / Xu, Jingzehua; Xie, Guanwen; Ding, Yimian et al.
Special Track on AI Alignment. ed. / Toby Walsh; Julie Shah; Zico Kolter. 28. ed. Association for the Advancement of Artificial Intelligence, 2025. p. 29538-29540 (Proceedings of the AAAI Conference on Artificial Intelligence; Vol. 39, No. 28).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Xu, Jingzehua

AU - Xie, Guanwen

AU - Ding, Yimian

AU - Zeng, Yongming

AU - Wang, Haoyu

AU - Zhang, Shuai

PY - 2025/4/11

Y1 - 2025/4/11

N2 - 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.

AB - 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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DO - 10.1609/aaai.v39i28.35317

M3 - Conference contribution

AN - SCOPUS:105003903925

T3 - Proceedings of the AAAI Conference on Artificial Intelligence

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Y2 - 25 February 2025 through 4 March 2025

ER -

Xu J, Xie G, Ding Y, Zeng Y, Wang H, Zhang S. UACOF: A USV-AUV Collaboration Framework for Underwater Tasks under Extreme Sea Conditions. In Walsh T, Shah J, Kolter Z, editors, Special Track on AI Alignment. 28 ed. Association for the Advancement of Artificial Intelligence. 2025. p. 29538-29540. (Proceedings of the AAAI Conference on Artificial Intelligence; 28). doi: 10.1609/aaai.v39i28.35317

UACOF: A USV-AUV Collaboration Framework for Underwater Tasks under Extreme Sea Conditions

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