Task-Oriented Sensing and Covert Transmissions for Collaborative Multi-AUV Systems

2026-07-15

AI Summary

This work addresses the challenge in underwater multi-AUV cooperative missions where active sensing risks target exposure while passive sensing suffers from incomplete information and low efficiency. Existing approaches struggle to effectively quantify the actual contribution of perceptual information to mission success under realistic covert communication constraints. To overcome this, the paper proposes the SVR-MARL framework, which explicitly models task-oriented sensing value and integrates it into multi-agent reinforcement learning—departing from conventional communication optimization that focuses solely on link performance or idealized information flow. By incorporating underwater covert communication constraints, the method learns distributed cooperative policies that significantly enhance task efficiency in covert localization and tracking scenarios, while simultaneously reducing communication overhead and exposure risk.

Abstract

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.

Problem

• covert communication
• multi-AUV systems
• passive sensing
• task-oriented perception
• information utility

Innovation

• SVR-MARL
• covert communication
• task-oriented sensing
• multi-AUV cooperation
• information value