Each vessel has its own way of sailing in the port region. Any autonomous vessel navigating such a scene should be able to predict the trajectories of surrounding ships and adjust its behaviour to avoid a collision. In this paper, combined with the sequence prediction method, a Long Short-Term Memory (LSTM) model is proposed to predict the trajectories of the vessels. The ground-truth Automatic Identification System (AIS) data in the port of Tianjin, China are used to train and test the proposed model. The experimental results prove that our model can predict ship trajectories accurately, and it is applicable to the autonomous navigation system.

To detect the abnormal behaviour of ships in the waters of any jurisdiction and to improve the safety of maritime navigation, the meshing-based method is adopted to obtain discrete trajectory data and a probabilistic directed graph model is established to obtain historical data from ships' AIS (automatic identification systems). The state statistical characteristics of each node in the ship probability map are obtained to detect the navigation state of the ship in real time. By predicting the normal navigation trajectory of the ship, it can be judged whether the ship has the potential to behave abnormally at some moment in the future. Simulation experiments were designed based on a maritime simulator platform. The experimental results indicate that the model can correctly predict abnormal behaviour by ships, including excessive speed and deviation from the channel or normal sailing mode.

In this paper, under parametric uncertainties and complex disturbances, a leader–follower formation control strategy based on accurate disturbance observer (ADO) and a novel fixed-time fast terminal sliding mode (FTFTSM) control for unmanned surface vehicles (USVs) is proposed. The main contributions of this paper are: (1) A novel fixed-time fast terminal sliding mode tracking control (FTFTSM-TC) strategy is designed for the tracking control subsystem, which greatly improves the convergence rate of the leader USV in trajectory tracking. (2) An ADO is designed to observe lumped disturbances with the smallest approximation error. The ADO greatly reduces the interference of disturbances and improves the performance of the formation system. (3) An ADO-based fixed-time formation control (ADO-FTFC) strategy is developed for the formation control subsystem to maintain the desired formation. Stability of the formation control system is established by the Lyapunov theory. Simulation results show that the proposed control strategy is superior for the USVs formation control.

The autonomous decision-making model for ship navigation requires extensive interaction and trial-and-error in real, complex environments to ensure optimal decision-making performance and efficiency across various scenarios. However, existing approaches still encounter significant challenges in addressing the temporal features of state space and tackling complex dynamic collision avoidance tasks, primarily due to factors such as environmental uncertainty, the high dimensionality of the state space, and limited decision robustness. This paper proposes an adaptive temporal decision-making model based on reinforcement learning, which utilizes Long Short-Term Memory (LSTM) networks to capture temporal features of the state space. The model integrates an enhanced Proximal Policy Optimization (PPO) algorithm for efficient policy iteration optimization. Additionally, a simulation training environment is constructed, incorporating multi-factor coupled physical properties and ship dynamics equations. The environment maps variables such as wind speed, current velocity, and wave height, along with dynamic ship parameters, while considering the International Regulations for Preventing Collisions at Sea (COLREGs) in training the autonomous navigation decision-making model. Experimental results demonstrate that, compared to other neural network-based reinforcement learning methods, the proposed model excels in environmental adaptability, collision avoidance success rate, navigation stability, and trajectory optimization. The model’s decision resilience and state-space mapping align with real-world navigation scenarios, significantly improving the autonomous decision-making capability of ships in dynamic sea conditions and providing critical support for the advancement of intelligent shipping.

Efficient coordinated scheduling has long been a focal point in port research, complicated by the diverse optimization goals dictated by different port characteristics. This study focuses on Yangtze River ports, exploring coordinated scheduling amidst river–sea intermodal transportation. Our research aims to reduce berth deviation costs and shorten the total scheduling time for ships, while maximizing berth utilization rates for ports. Initially, we analyzed the operational realities of Yangtze River ports and waterways. Subsequently, we innovatively introduced three key factors influencing scheduling: berth preferences, seagoing ship inspections, and planning cycles. Finally we proposed the optimized Non-dominated Sorting Genetic Algorithm III (NSGA-III). Evaluating the model using a seven-day dataset of vessel activities at Yangtze River ports revealed significant improvements: the optimized NSGA-III enhanced objective values by 30.81%, 13.73%, and 12.11% compared to the original scheduling approach, surpassing both conventional NSGA-III and NSGA-II algorithms. This study underscores the model’s efficacy in not only reducing operational costs through optimized ship and berth sequencing but also in enhancing clearance efficiency for relevant authorities.

In this paper, we present a novel nonlinear model predictive control (NMPC) algorithm based on the Laguerre function for dynamic positioning ships to solve the problems of input saturation, unknown time-varying disturbances, and heavy computation. The nonlinear model of a dynamic positioning ship is presented as a linear model, transformed from a standard affine nonlinear state-space model by precise feedback linearization. The environmental disturbance is overcome using an integrator. The time cost of the proposed nonlinear control algorithm is decreased by inducing the Laguerre function to describe the feedback-linearization system input increments. The Laguerre function reduces the matrix dimensions of the nonlinear optimization problem. The simulation results for a DP supply vessel showed that the novel algorithm maintained the effective control performance of the original nonlinear model predictive control algorithm and had a reduced computation load to satisfy the requirements of real-time operation.