Despite receiving much attention from researchers in the field of naval architecture and marine engineering since the early stages of modern shipbuilding, the berthing phase is still one of the biggest challenges in ship maneuvering due to the potential risks involved. Many algorithms have been proposed to solve this problem. This paper proposes a new approach with a path-planning algorithm for automatic berthing tasks using deep reinforcement learning (RL) based on a maneuvering simulation. Unlike the conventional path-planning algorithm using the control theory or an advanced algorithm using deep learning, a state-of-the-art path-planning algorithm based on reinforcement learning automatically learns, explores, and optimizes the path for berthing performance through trial and error. The results of performing the twin delayed deep deterministic policy gradient (TD3) combined with the maneuvering simulation show that the approach can be used to propose a feasible and safe path for high-performing automatic berthing tasks.

This study presents a pressure variation model (PVM) derived from the regression analysis of dynamic pressure computed through numerical analysis to estimate the velocity of underwater vehicles. Furthermore, the drift angle estimation algorithm was developed using predicted velocities from PVM and pressure sensor differences. This approach estimates the single-motion states of underwater vehicles, such as straight, turning, and gliding. Furthermore, it confirms the viability of state estimation even in multiple motions involving turning and gliding motion with a drift angle and spiral motion. The comparison with numerical analysis results validated prediction accuracy within 15%.

Among the 6 degrees of freedom (6-DoF), excessive roll motion is the most dangerous cause of ships capsizing. However, when analyzing the maneuverability of surface ships, the roll components have usually been ignored. It is widely known that the influence of roll moment becomes significant for surface ships with low GM (metacentric height) and high speed. This paper examines the pure roll test for several surface ships to assess the roll-related hydrodynamic derivatives of added mass and damping in maneuvering. The objective ships are the KRISO Container Ship (KCS), David Taylor Model Basin (DTMB), Office of Naval Research Tumblehome (ONRT), and Delft 372 catamaran, where the DTMB and ONRT ships are equipped with complementary bilge keels as damping devices and have a small GM, which the Delft 372 catamaran does not have. The flow during pure roll is analyzed by the Computational Fluid Dynamics (CFD) simulation method that allows the complex flow around ships to be captured, especially when the bilge keel and skeg are considered. The results indicate that the roll moment is greatest in the catamaran. Since the roll moments of the DTMB and ONRT are larger than that of the KCS, bilge keels and surface shape also contribute to increasing roll damping moment. In addition, a comparison of the damping derivatives due to roll rate with results obtained from another method indicates that CFD simulation is capable of accurately predicting the roll-related derivatives, which is difficult to perform by the experiment method.