The field of underwater robotics and networks is experiencing significant advancements, particularly in the areas of intelligent network architectures, simulation capabilities, and formation control strategies. Recent developments are focusing on enhancing the adaptability, intelligence, and multifunctionality of underwater systems through innovative technologies such as Digital Twin (DT) and multi-agent reinforcement learning. These technologies are being leveraged to improve the timeliness, robustness, and flexibility of resource allocation and multi-task scheduling algorithms in underwater acoustic sensor networks (UASNs). Additionally, there is a growing emphasis on the integration of Artificial Intelligence (AI) into simulation tools, enabling more realistic and efficient troubleshooting and optimization of underwater network protocols and communication technologies. Formation control strategies are also advancing, with new approaches being developed for identity-less distributed shape control and multi-robot pursuit scenarios, which enhance the adaptability and resilience of underwater robot swarms. These innovations are paving the way for more efficient and intelligent underwater operations, with potential applications in environmental monitoring and defense.

Noteworthy papers include one that introduces a Digital Twin-based Network Architecture (DTNA) for UASNs, significantly improving resource allocation and AI algorithm training. Another notable contribution is the development of the Fourth Generation (FG) ns-3-based simulator Aqua-Sim~FG, which enhances the general and intelligent simulation ability for underwater acoustic networks. Additionally, the paper on Leader-Follower 3D Formation for Underwater Robots demonstrates experimental realizations of complex 3D formations using a vision-based perception system, laying the groundwork for future applications of underwater robot swarms.