State-of-the-Art Underwater Vehicles and Technologies Enabling Smart Ocean: Survey and Classifications
Jiajie Xu, Xabier Irigoien, Mohamed-Slim Alouini
TL;DR
This paper addresses the need for a cohesive understanding of state-of-the-art underwater vehicles and enabling technologies to realize a connected Smart Ocean. It systematically classifies UVs (ROVs, AUVs, HUVs, USVs, gliders, and UBRs) and surveys supporting communications, docking, and sensing infrastructure, highlighting their capabilities and limitations. The authors discuss how AI, machine learning, and advanced sensing can enhance autonomy, efficiency, and resilience for diverse missions. The work underscores design challenges—energy efficiency, deep-water communications, and environmental robustness—and outlines pathways toward scalable, real-time ocean monitoring and sustainable resource management.
Abstract
The exploration and sustainable use of marine environments have become increasingly critical as oceans cover over 70% of surface of Earth. This paper provides a comprehensive survey and classification of state-of-the-art underwater vehicles (UVs) and supporting technologies essential for enabling a smart ocean. We categorize UVs into several types, including remotely operated vehicles (ROVs), autonomous underwater vehicles (AUVs), hybrid underwater vehicles (HUVs), unmanned surface vehicles (USVs), and underwater bionic vehicles (UBVs). These technologies are fundamental in a wide range of applications, such as environmental monitoring, deep-sea exploration, defense, and underwater infrastructure inspection. Additionally, the paper explores advancements in underwater communication technologies, namely acoustic, optical, and hybrid systems, as well as key support facilities, including submerged buoys, underwater docking stations, and wearable underwater localization systems. By classifying the vehicles and analyzing their technological capabilities and limitations, this work aims to guide future developments in underwater exploration and monitoring, addressing challenges such as energy efficiency, communication limitations, and environmental adaptability. The paper concludes by discussing the integration of artificial intelligence and machine learning in enhancing the autonomy and operational efficiency of these systems, paving the way for the realization of a fully interconnected and sustainable Smart Ocean.