The field of autonomous maritime systems is experiencing significant growth, driven by innovations in control allocation, reinforcement learning, and physics-informed neural networks. Researchers are exploring new approaches to improve the navigation and control of autonomous surface vehicles (ASVs) and autonomous underwater vehicles (AUVs) in complex environments. Notably, the development of novel control frameworks and AI-enhanced design tools is enabling the creation of more efficient and maneuverable underwater gliders. These advancements have the potential to transform various aspects of maritime operations, including environmental monitoring, ocean exploration, and search and rescue missions. Some noteworthy papers in this area include:

• A finite volume Simo-Reissner beam method for moored floating body dynamics, which simulates mooring cables using non-linear beam models implemented in a finite volume framework.
• AI-Enhanced Automatic Design of Efficient Underwater Gliders, which introduces an automated design framework for creating underwater robots with non-trivial hull shapes.