协作多自主水下航行器系统的面向任务感知与隐蔽传输

中文摘要 AI 辅助

在水下隐蔽协作任务中，自主水下航行器（AUV）常无法依赖主动声纳持续获取完整信息，因其主动感知和频繁通信会增加暴露风险，主要依赖被动观察导致局部感知不完整且任务效率受限。水下声学通信虽能通过信息共享缓解此局限，但受长时延、强干扰、低可靠性及隐蔽暴露风险制约。现有面向通信的多智能体强化学习研究常将通信建模为理想信息流，传统通信优化主要关注链路级性能，均不足以刻画实际条件下感知信息对协作任务的贡献。本文提出传感信息价值实现多智能体强化学习（SVR-MARL）框架，利用实际信息刻画信息对协作任务的效用，并在现实通信和隐蔽约束下学习分布式协作策略。通过隐蔽多AUV协作定位与跟踪的案例研究，证明了该框架在提高协作任务效率、降低不必要通信和暴露风险方面的潜力。

英文摘要

In underwater covert cooperative missions, autonomous underwater vehicles (AUVs) often cannot rely on active sonar to continuously obtain complete information, since active sensing and frequent communications increase the risk of exposure. As a result, AUVs primarily rely on passive observation, an approach that yields incomplete local perception and limited task efficiency. Although underwater acoustic communications can mitigate this limitation through information sharing, they are simultaneously constrained by long delays, severe interference, low reliability, and the risk of covert exposure. Existing communications-oriented multi-agent reinforcement learning (MARL) studies often model communication as an ideal information flow, whereas traditional communication optimization primarily focuses on link-level performance. However, both are insufficient to characterize the actual contribution of perceptual information to cooperative tasks under realistic conditions of covert physical communications. This paper proposes a Sensed Information Value Realization Multi-Agent Reinforcement Learning (SVR-MARL) framework that leverages practical information to characterize the utility of information for cooperative tasks and learns distributed cooperative policies under realistic communication and covert constraints. Through a case study of covert multi-AUV cooperative localization and tracking, the potential of the proposed framework to improve collaborative task efficiency while reducing unnecessary communication and exposure risks is demonstrated.

发表机构

• School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机科学学院）
• School of Cyber Engineering, Xidian University（西安电子科技大学网络空间安全学院）
• Harbin Engineering University（哈尔滨工程大学）
• Department of Informatics and Telecommunications, National and Kapodistrian University of Athens（雅典国立卡波迪斯特里亚大学信息与电信系）
• KU 6G Research Center, Department of Computer and Information Engineering, Khalifa University（哈利法大学KU 6G研究中心计算机与信息工程系）
• CentraleSupelec, University Paris-Saclay（巴黎萨克雷大学中央理工学院）
• School of Electrical and Electronics Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）

机构由 AI 辅助整理，请以论文原文为准。