For efficient and safe navigation for an autonomous surface vehicle (ASV), this paper proposes an autonomous navigation behavior framework that integrates deep reinforcement learning (DRL) to achieve autonomous decision-making and low-level control actions in path following and collision avoidance. By controlling both the propeller speed and the rudder angle, the policy of each behavior pattern is trained with the soft actor–critic (SAC) algorithm. Moreover, a dynamic obstacle trajectory predictor based on the Kalman filter and the long short-term memory module is developed for obstacle avoidance. Simulations and physical experiments using an under-actuated very large crude carrier (VLCC) model indicate that our DRL-based method produces appreciable performance gains in ASV autonomous navigation under environmental disturbances, which enables forecasting of the expected state of a vessel over a future time and improves the operational efficiency of the navigation process.
The use of Satellite-Derived Bathymetry (SDB) constitutes an efficient, cost-effective, time-saving, and scalable approach for generating high-resolution shallow-water bathymetry. In this context, SDB can be proven to be a valuable method for supporting bathymetric surveys of sub…
Navigation lock manifolds are key components of filling-and-emptying systems, and port-flow distribution affects chamber flow stability and filling efficiency. Under unsteady filling conditions, port-flow distribution is governed by discharge variation and manifold geometry, maki…
Autonomous Underwater Vehicles (AUVs) equipped with multibeam echosounders (MBESs) are deployed in oceans in expeditions worldwide to find shipwrecks, as they can survey the seafloor at the resolution required to identify such objects. Due to the severely constrained acoustic ban…
For stable operation of autonomous surface ships, real-time anomaly detection of engine conditions must be coupled with an operational framework that sustains model performance in dynamic maritime environments. This study proposes an autonomous maintenance system that combines a…
Autonomous Underwater Vehicles (AUVs) are increasingly used in deep-sea exploration, environmental monitoring, and marine engineering. Their operational safety and mission performance rely heavily on accurate and long-endurance underwater localization. However, both single-sensor…
Satellite remote sensing increasingly supports water quality monitoring, yet the temporal transferability of machine learning (ML) models remains insufficiently tested, particularly in coastal shallow lakes subject to hydrological variability. This study evaluates the predictive…