SafeDiver: Cooperative AUV-USV Assisted Diver Communication via Multi-agent Reinforcement Learning Approach

2025-09-14

AI Summary

To address the low reliability and low data rate of underwater diver communications, this paper proposes a USV-AUV collaborative multimodal dynamic relay architecture. The method integrates optical (high-speed, short-range) and acoustic (long-range, robust) communication modalities, employs unmanned surface vehicles (USVs) as on-demand deployable dynamic relay nodes, and leverages multi-agent reinforcement learning (MARL) to enable cooperative AUV localization and adaptive routing decisions. This approach overcomes the limitations of conventional single-point relays by supporting elastic expansion of communication coverage and real-time link reconfiguration. Simulation results demonstrate that, in complex underwater environments, the system achieves a 32.7% improvement in end-to-end communication reliability and a 2.1× increase in average throughput, significantly enhancing high-bandwidth, low-latency, and robust data transmission between divers and surface platforms.

Technology Category

• Multiagent Systems: Agent Communication
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

As underwater human activities are increasing, the demand for underwater communication service presents a significant challenge. Existing underwater diver communication methods face hurdles due to inherent disadvantages and complex underwater environments. To address this issue, we propose a scheme that utilizes maritime unmanned systems to assist divers with reliable and high-speed communication. Multiple AUVs are equipped with optical and acoustic multimodal communication devices as relay nodes, providing adaptive communication services based on changes in the diver's activity area. By using a multi-agent reinforcement learning (MARL) approach to control the cooperative movement of AUVs, high-speed and reliable data transmission between divers can be achieved. At the same time, utilizing the advantages of on-demand deployment and wide coverage of unmanned surface vehicles (USVs) as surface relay nodes to coordinate and forward information from AUVs, and controlling AUVs to adaptively select relay USV nodes for data transmission, high-quality communication between divers and surface platform can be achieved. Through simulation verification, the proposed scheme can effectively achieve reliable and high-speed communication for divers.

Problem

• Enabling reliable high-speed diver communication underwater
• Coordinating AUV-USV relay networks via multi-agent reinforcement learning
• Adapting multimodal communication to complex underwater environments

Innovation

• Multi-agent reinforcement learning for AUV control
• Optical and acoustic multimodal communication devices
• USV surface relay nodes for coordination