Search Results (154)

High-fidelity ship dynamics models are essential for the reliable operation of maritime autonomous systems. However, existing Markov-based maneuvering models and purely data-driven predictors struggle to capture hydrodynamic memory and degrade under non-ideal sensing. To address these challenges, this paper proposes a novel approach for robust ship motion prediction, the Non-Markovian Memory-Augmented Environment-Perceived and Physics-Informed Network (NMA-EPIN). This method explicitly models long-term hydrodynamic dependencies through a memory-augmented architecture. Within NMA-EPIN, a Control-Physics-Informed Neural Network (CPINN) paradigm enforces velocity–position kinematic consistency and control-logic alignment as soft constraints, suppressing cumulative drift under degraded observations. Experiments on a high-fidelity simulated dataset show that NMA-EPIN attains an average coefficient of determination $R^2=0.977$ under nominal conditions, effectively eliminating the position drift observed in baselines. Under extreme compound perturbations (50% sensor noise, packet loss, and delays), NMA-EPIN retains $R^2\approx 0.91$, which significantly outperforms the baselines. Full article

Ship transit in vertical ship lift chambers represents a highly confined flow regime characterized by extreme blockage (N < 2), where ship-induced piston effects can significantly influence navigational safety and structural loads. This study presents an experimental investigation of the unsteady hydrodynamic responses of a 1000 t class ship operating in the Baise vertical ship lift. A 1:10 large-scale physical model was constructed to reproduce the ship lift chamber and auxiliary lock geometry under Froude similarity. Tests were conducted for prototype water depths of 3.7–3.9 m and sailing velocities between 0.4 and 1.1 m/s. Ship sinkage, free-surface oscillations, and dynamic chamber weight variations were synchronously measured. Results revealed a profound process asymmetry: the exit maneuver induced significantly higher sinkage (0.92 m at 1.1 m/s) and chamber weight fluctuations (810 t) than the entry process due to restricted return flow replenishment. A non-dimensional predictive P–K relationship was derived with a regression coefficient α = 1.9121. Based on safety margins and mechanical load limits, critical speed thresholds were established at 0.6 m/s for exit and 0.7 m/s for entry to ensure a minimum safety clearance of 0.48 m even under docking error conditions. Full article

The paper concerns motion modeling for full-scale ships under the frame of system identification (SI) principles. Several groups of full-scale ship maneuvering experiments have been implemented to collect research data. On structure identification, as an innovation, a nonlinear integrating ship motion model is identified and established. The concerned model includes 21 parameters. Under the premise of error criterion, a batch least-squares (BLS)-based parameter estimation process is used to estimate the 21 parameters. The strategy is verified for feasibility and availability by using a pragmatic case study. The accuracy of the estimated parameter values is checked by comparing the track in simulation with the trial trajectory. Research indicates that the technical process proposed in the paper from the perspective of SI principles can be applied to the modeling of ship maneuvering motion. Full article

Under strict spatial constraints and environmental interference, autonomous navigation of vessels in inland bridge-restricted waterways demands precise coordination between collision avoidance and trajectory tracking. To meet these operational demands, an integrated framework that directly combines spatiotemporal risk assessment with dynamic control execution is developed. Based on a digital traffic model integrating bridge piers and channel boundaries, collision risks are evaluated by combining trajectory-predicted time to safe distance with the velocity obstacle interval. Such a formulation quantifies the actual spatial difficulty of evasion rather than relying solely on temporal urgency. Driven by this continuous assessment, a time-series rolling strategy calculates feasible maneuvering intervals, generating trajectories that comply strictly with inland navigation rules and physical vessel limits. Subsequently, an adaptive model predictive control algorithm executes these commands, implicitly compensating for the localized hydrodynamic disturbances typical of bridge areas. The effectiveness of the architecture is validated through comprehensive simulations covering rule-based encounters and complex multi-vessel scenarios. Quantitative results indicate that under wind and current disturbances, the maximum route tracking deviation is constrained below 53 m, while the minimum encounter distance with target ships is consistently maintained above 51 m. These performance metrics confirm the capacity to execute safe, rule-compliant maneuvers while preserving high navigational precision in confined inland environments. Full article

Planning effective collision avoidance routes is a crucial measure for ensuring ship safety. However, position uncertainty caused by sensor noise, communication delays, and sudden changes in the maneuvering of target vessels severely restricts the reliability of traditional collision avoidance methods. To address this, this study integrates the velocity obstacle method and conditional value at risk theory to design a ship collision avoidance framework under position uncertainty. The position uncertainty of the target vessel is modeled using a Gaussian distribution. By fusing multi-source sensor data from radars and the Automatic Identification System through Bayesian inference, the posterior estimate of the vessel’s position is dynamically updated, thereby constructing an uncertainty velocity obstacle region. The Gaussian posterior distribution of the position is incorporated into a stochastic loss function to formulate a stochastic optimization model that balances navigation efficiency and collision risk. The model is solved using the sample mean approximation method and strictly complies with the International Regulations for Preventing Collisions at Sea. The results of two sets of multi-vessel encounter simulations demonstrate that, compared with traditional methods, the proposed method achieves superior performance in terms of total path length and algorithm runtime. It is capable of generating compliant collision avoidance strategies in complex dynamic crossing scenarios, attaining optimal comprehensive performance with respect to safety, economy, and regulatory compliance. Full article

Ship maneuvering models based on MMG or Abkowitz formulations often suffer from systematic mismatches under real operating conditions, where shallow water, hull fouling, rudder degradation, and wind loads may coexist. This study proposes a physics-structured residual learning framework for multi-source disturbance decomposition and compensation. Disturbance-specific expert networks are introduced to map different disturbance sources into separate residual channels. A CNN-SE-BiLSTM encoder is further designed to estimate the slowly varying latent disturbance states from residual sequences, whereas wind is treated through an external pathway owing to its directly measurable and higher-frequency nature. Simulations on the KVLCC2 benchmark vessel under single-source, triple-source, and wind-inclusive disturbance scenarios demonstrate stable long-horizon closed-loop autoregressive prediction, with position-RMSE reductions of 74.7–91.7% relative to the corresponding nominal-MMG and wind-ablation baselines. These results indicate that the proposed physics-structured residual learning framework improves long-horizon prediction accuracy while retaining interpretable and modular disturbance-specific correction channels under complex operating conditions. Full article

Predicting the maneuverability of ships equipped with twin azimuth thrusters remains challenging due to their complex hydrodynamic interactions. This study develops an integrated framework that combines Computational Fluid Dynamics (CFD) with an enhanced Manoeuvring Mathematical Group (MMG) Model. Using the platform supply vessel Hai Yang Shi You 661 as a case study, all requisite hydrodynamic derivatives and propeller coefficients were efficiently obtained through CFD-based captive model tests, including oblique towing and Planar Motion Mechanism tests, conducted in STAR-CCM+ 2206. A core contribution of this work is the systematic evaluation of how hydrodynamic model fidelity affects prediction accuracy. Numerical turning circle simulations were executed with three models of increasing complexity: one with only linear derivatives, a second incorporating nonlinear higher-order terms, and a third, full model that additionally includes nonlinear velocity coupling terms. The results, rigorously validated against full-scale trial data, demonstrate that while the basic CFD-MMG approach is feasible, the inclusion of nonlinear coupling terms is critical for achieving accurate predictions in large-amplitude maneuvers. This enhancement reduced the maximum error in tactical diameter prediction from over 25% to approximately 11.8%. Consequently, this study provides a validated and cost-effective framework for maneuvering the prediction of azimuth-thruster vessels and offers clear, quantitative guidance on the necessary level of model complexity for practical engineering applications. Full article

To improve ship AIS trajectory prediction under pronounced spatiotemporal coupling and dynamic maneuvering conditions, this study proposes a Spatio-Temporal-Hybrid-Multi-head Attention model (STHA) integrating multiscale convolution, bidirectional long short-term memory, and multi-head attention. Historical AIS data from the Zhoushan waters in 2024 were preprocessed through screening, cleaning, outlier removal, resampling, and cubic spline interpolation to construct trajectory samples. Comparative experiments were conducted against BP, BiLSTM, and BiGRU using MAPE, RMSE, and R² as evaluation metrics. The results show that STHA achieves the best overall predictive performance, more accurately follows trajectory variations across different vessel types, and exhibits better robustness in scenarios involving turning and speed changes. These findings indicate that the proposed model is effective for high-precision ship trajectory prediction and can provide useful support for subsequent collision risk assessment and navigation safety assistance. Full article

Conventional heading-keeping autopilot logic exhibits well-known performance limitations under complex route geometry and environmental disturbances. Motivated by this limitation, this paper proposes a line-adhering intelligent navigation framework for disturbance-aware path-following of autonomous ships. The core idea is based on numerical simulation scenarios representing curved inland/coastal routes under wind- and current-disturbance conditions. The addressed gap lies in the limited integration of route-geometry adherence, human-like maneuvering logic, and disturbance-aware controller reconfiguration within conventional heading-centered ship path-following frameworks. Therefore, a rough-set classifier identifies disturbance modes and reconfigures PID, LQR, and MPC controllers in real time. Moreover, a vessel-dynamics constrained Bézier refinement method generates high-resolution reference paths aligned with navigational curvature limits. Mathematical models including the Nomoto and MMG formulations are incorporated to ensure controllability and dynamic feasibility. Results show that the proposed framework improves path-following precision, robustness, and comfort under the considered simulation conditions. Full article

To address the strongly nonlinear hydrodynamic coupling and complex maneuvering challenges encountered by large ships during berthing operations in restricted waters, this paper proposes a high-precision autonomous berthing control system incorporating four-quadrant propeller hydrodynamics. Based on an improved Mathematical Maneuvering Group (MMG) framework, a three-degree-of-freedom (3-DOF) dynamic model is established to accurately capture the transient thrust and torque mappings of the propeller over all four quadrants. A dynamic line-of-sight (LOS) guidance system with a nonlinearly decaying acceptance radius is tightly coupled with PD/PI controllers to coordinate and regulate the rudder angle and propeller rotational speed. The numerical solver was rigorously validated against turning-test data for the S-175 container ship, with the errors of the key parameters all controlled within 15%. Subsequently, under the environmental conditions of Yangshan Port, full-condition path-planning and berthing simulations were conducted for the novel B-573 container ship under steady-current disturbances. These simulations evaluated multiple flow directions, namely due south, due north, due west, and due east defined in the Earth-fixed coordinate system, as well as multiple intensity levels ranging from 0 to 1.5 m/s that were specifically tested under the due north current. Quantitative evaluation shows that, under the highly challenging current condition of 1.0 m/s, the dynamic corrective mechanism effectively drives the global mean absolute error (MAE) to converge to 85.50 m, representing a 62% statistical reduction relative to the transient peak value. In addition, a parameter sensitivity analysis based on the cumulative cross-track error confirms that, when subject to variations in the underlying hydrodynamic parameters, the proposed system can suppress fluctuations in trajectory error to a very low level, thereby demonstrating a certain degree of control robustness. During the terminal berthing stage, the vessel smoothly completed an extreme deceleration from an initial speed of 6.4 m/s to a full stop within 588 s, while constraining the maximum astern rotational speed to −2 rps and seamlessly passing through all four propeller quadrants. The results confirm that the proposed autopilot framework possesses a certain degree of engineering feasibility in complex maritime environments. Full article

Automatic Identification System (AIS) data provide valuable insights into ship behavior, supporting maritime safety, situational awareness, and operational efficiency capabilities that are increasingly required for autonomous ship functions and harbor maneuvering assistance. This review synthesizes recent research on AIS-based ship behavior analysis and modeling published between 2022 and 2024 using a structured literature search and screening process informed by PRISMA principles. The review presents a five-stage workflow, spanning data processing, data analysis, knowledge extraction, modeling, and runtime applications with emphasis on how these stages contribute to perception, prediction, and decision support in automated navigation. Four dimensions are considered in data analysis, including statistical analysis, safety indicators, situational awareness, and anomaly detection. The modeling approaches are categorized into classification, regression, and optimization, highlighting current limitations such as data quality, algorithmic transparency, and real-time performance, while also assessing runtime feasibility for onboard or edge deployment. Three runtime application directions are identified: autonomous vessel functions, remote monitoring and control operations, and onboard decision-support tools, with numerous studies focusing on constrained waterways and port-approach scenarios. Future directions suggest integrating multi-source data and advancing machine learning models to improve robustness in complex traffic and harbor environments. By linking theoretical insights with practical onboard needs, this study provides guidance for developing intelligent, adaptive, and safety-enhancing maritime systems. Full article

This paper presents an analysis of the risk of failure of port structures in a modern seaport due to vessel impacts. The analysis addresses potential damage related to port maneuvers of self-maneuvering vessels and possible risk reduction options that can be applied to enhance port resilience. The proposed system model—including ship, port infrastructure, and environment—enabled the observation of both implemented and anticipated future risk reduction measures. The analysis was carried out using the ferry terminal in the large Polish Port of Gdynia as a case study. A Bayesian influence diagram—including decisions related to the implementation of risk reduction options—was used to determine the total risk associated with Ro-Pax ferry port calls. Sustainable risk management led to the implementation of a cloud-based monitoring system and, subsequently, to the design of a new terminal in line with the green port concept. The main result of the study was a quantitative assessment of the risk of damage to port infrastructure caused by ferries, related to ship maneuvering operations. A comparative assessment of the two locations demonstrated improved safety and reduced environmental pollution in the new Public Ferry Terminal. This improvement was made possible mainly by reduced spatial risk and the implementation of cold-ironing technology. Full article

Wind-assisted ship propulsion (WASP) systems are gaining importance due to their contribution to reducing greenhouse gases and saving fuel. Existing studies mostly focus on the aerodynamics of sailing systems, the integration of sails and ship dynamics, and the prediction of fuel savings. The present study extends the use case of sailing systems by proposing a new control logic that improves maneuvering performance. Determining the spin ratio of rotor sails not only with thrust but also with side forces and moments is also included as an objective function. Using numerous random weights for each term and environmental conditions, the turning performance of the target ship was evaluated. Then, an artificial neural network (ANN) model was trained to decide on the optimal weights, depending on the environmental conditions. Finally, the performance of the new control approach was evaluated based on turning and zigzag test simulations. It was found that the advance, transfer, and tactical diameters dropped by up to 5%, 7% and 7%, respectively, compared to those of a conventional ship. When it comes to the zigzag performance, it was revealed that the overshoot angles dropped even though there was no simulation data about zigzag tests in the trained ANN model. Thus, it was shown that sails improve the maneuverability of ships in addition to providing additional thrust if a proper control approach is adopted. Full article

The navigation of ships in complex shallow water areas is constrained by various factors such as water depth, channel boundaries, and environmental interference. Therefore, it is crucial to improve the adaptability and effectiveness of collision avoidance decisions for ships in complex shallow water scenarios. To address these issues, this paper proposes a multi-factor constrained autonomous decision-making method for complex shallow water vessel maneuvering. Firstly, a digital transportation environment was constructed by combining dynamic and static information, such as water depth, tides, channel boundaries, changes in maneuvering characteristics, and navigation rules, and a navigable water area model that was suitable for shallow water was proposed. Then, considering the constraints of ship maneuverability and the navigation environment, a shallow water ship motion model affected by wind flow was developed. A complex shallow water adaptive maneuvering coupled decision-making method was constructed, considering the influence of ship navigation rules and channel constraints. This method utilizes the Kalman filtering algorithm to correct residuals and predict the maneuvering of the target vessel. Integrated improved heading control and guidance algorithms achieved automatic heading control and future position prediction. Through testing and verification in the complex waters of the Yangtze River estuary, the results show that the autonomous collision avoidance decision-making method proposed in this paper can effectively make collision avoidance decisions in complex multi-ship shallow water areas. This study can provide innovative and practical solutions for the technological development of autonomous ship collision avoidance decision-making. Full article

This study investigates the effects of roll, pitch, and heave on the motion characteristics of a fully wind-powered ship equipped with two rigid wing sails. While previous research by the authors demonstrated that L-shaped and T-shaped sail arrangements improve thrust generation and maneuverability, the importance of six-degree-of-freedom (6-DOF) motion modeling has not been fully clarified. To clarify this open question, the present work provides a systematic comparison between the 6-DOF model and a simplified three-degree-of-freedom (3-DOF) model in which roll, pitch, and heave are constrained. Four sail configurations are analyzed under true wind directions of 150° and 180°. The comparison reveals that the 3-DOF model cannot accurately reproduce key features of the ship’s trajectory, drift angle, and speed, particularly for cases where aerodynamic–hydrodynamic coupling strongly affects yaw stability. In contrast, the 6-DOF simulations reveal substantially different steady-state behavior and demonstrate that roll, pitch, and heave play an essential role in predicting maneuvering performance. The results clarify how sail arrangement and motion modeling interact to shape the maneuvering characteristics of fully wind-powered vessels, providing fundamental insights for the development of reliable 6-DOF simulation frameworks and for the design assessment of next-generation wind-propelled ships. Full article