Ship Maneuvering Research Articles

Estimating Hydrodynamic Coefficients of Real Ships Using AIS Data and Support Vector Regression

In response to the complexity and time demands of conventional methods for estimating the hydrodynamic coefficients, this study aims to revolutionize ship maneuvering analysis by utilizing automatic identification system (AIS) data and the Support Vector Regression (SVR) algorithm. The AIS data were collected and processed to remove outliers and impute missing values. The rate of turn (ROT), speed over ground (SOG), course over ground (COG) and heading (HDG) in AIS data were used to calculate the rudder angle and ship velocity components, which were then used as training data for a regression model. The accuracy and efficiency of the algorithm were validated by comparing SVR-based estimated hydrodynamic coefficients and the original hydrodynamic coefficients of the Mariner class vessel. The validated SVR algorithm was then applied to estimate the hydrodynamic coefficients for real ships using AIS data. The turning circle test wassimulated from calculated hydrodynamic coefficients and compared with the AIS data. The research results demonstrate the effectiveness of the SVR model in accurately estimating the hydrodynamic coefficients from the AIS data. In conclusion, this study proposes the viability of employing SVR model and AIS data for accurately estimating the hydrodynamic coefficients. It offers a practical approach to ship maneuvering prediction and control in the maritime industry.

Self-organizing data-driven prediction model of ship maneuvering fast-dynamics

In this paper, to facilitate ship maneuvering fast-dynamics prediction which is critically required within motion planning and control, a self-organizing data-driven network with hierarchical pruning (SDN-HP) is innovated by virtue of fuzzy neural architecture. To be specific, the SDN-HP model is incrementally trained from an empty-hidden-node fuzzy neural network with recurrent velocities as inputs and outputs, and the rudder angle as exogenous command. With the aid of fuzzy neuron generation criteria, the SDN grows with increasing fuzzy rules such that fast-dynamics of ship velocities can be in-time fitted. To balance training accuracy and prediction generalization, hierarchical pruning mechanism is created by defining a new concept of fuzzy rule intensity, in order that hidden nodes with insignificant contributions can be dynamically removed, thereby enabling dynamics abstraction with parsimonious structure. With the aid of reference model of Esso Osaka tanker and KVLCC2 database, the SDN-HP model is sufficiently trained and applied to free-running maneuvering prediction in on-line manners. Experimental results show that the proposed SDN-HP framework achieves remarkable fast-dynamics prediction ability and outperforms state-of-the-art deep learning paradigm, e.g., LSTM and GRU.

Producing electricity on board a ship in motion or stationary using photovoltaic panels

A very important pillar for the world economy is the transport sector. Nowadays, about 80% of freight transport has been done by sea. Besides the recognized economic efficiency, ships are, unfortunately, an important sources of marine environment pollution. It is well known that the IMO is permanently concerned in maritime safety and many measures have been shown to be successful in reducing ship-sourced pollution. But it is not enough, new solutions must be found, and the answer can come from the direction of green energies (electricity produced from renewable sources). On board ships can be used as renewable energy sources wind energy or solar energy. Solar energy is easier to use because photovoltaic panels can be mounted on board the ship to produce electricity (however, provided that the photovoltaic panels do not increase the ship's forward resistance or reduce its manoeuvrability). Studies have shown that offshore wind energy is much more efficient than a ship-mounted wind turbine because an on-board wind turbine greatly reduces a ship's speed and manoeuvrability. The authors of the paper proposed to study the production of electrical energy obtained with the help of photovoltaic panels on board a ship in motion and stationary. The ship marched in the Mediterranean Sea for two months.

SeaBil: Self-attention-weighted ultrashort-term deep learning prediction of ship maneuvering motion

Accurate prediction of motion dynamics fundamentally promotes the autonomy of intelligent ships, but faces great challenges in modeling mechanism. In this paper, to establish data-driven recurrent mapping within ship motion dynamics, an ultrashort-term deep learning predictor is innovatively developed by elaborately creating a self-attention-weighted bidirectional long short-term memory (Bi-LSTM) network in conjunction with 1-dimensional convolution (Conv-1D), named SeaBil. To be specific, combined with the sliding-window technique, the Conv-1D is devised to convert 5-dimensional-input samples consisting of course angle, yaw rate, roll angle, total speed and rudder angle into 1-D-feature vectors, thereby extracting coupled feature maps within the current data-window. The Bi-LSTM is further deployed to recurrently learn forward and reverse feature maps of ship motion time-series data. Self-attention mechanism cascaded in the serial is employed to adaptively assign time-varying weights of sample instants within the current window such that the ultrashort-term prediction of course angle, yaw rate, roll angle and total speed can be achieved. Eventually, comprehensive comparisons to typical approaches using real-world samples demonstrate the superiority of SeaBil in terms of ultrashort-term prediction accuracy.

Design and Implementation of Engine Order Telegraph With RS485 Communication for Ship Maneuvering

EOT (Engine Order Telegraph) is the embodiment of a machine-human interface that is controlled by the captain of the ship for safe and sustainable ship maneuvers as a basic part of a connected remote propulsion system. Remote control systems on modern ships usually have a control transfer system that allows control to be transferred between locations. Remote control is usually possible from two locations: the platform and the Engine Control Room (ECR). Some ships do not have a remote control handle on the ECR. When in bridge control mode, the bridge handle directly controls the engine set point. When in Engine Control Room mode, the bridge handle sends a telegraph signal to the ECR and the ECR handle controls the control system set point. In local control, the remote control system is inactive and the bridge handle sends a telegraph signal to the local control position and the machine is operated by its manual control in the engine room. In this study, an EOT device will be realized using the Arduino Mega2560 microcontroller and data communication using the RS485 serial from the bridge to the engine control room.

Modeling the influence of wind on ship circulation elements

When solving a number of practical problems of navigation and ship maneuvering, it is necessary to take into account the effect of wind on the parameters of the ship motion, including curvilinear motion, in particular, on circulation. There is very little information on this problem, especially in terms of quantitative estimates of the wind impact on circulation elements. Based on this, the purpose of this work is to study the effect of wind on the circulation elements based on mathematical modeling and to determine quantitative estimates of this effect when changing such factors as wind speed and heading angle, ship length and rudder angle. The simulation is carried out on a certified navigation simulator Transas NT Pro-5000. The mathematical model of vessel movement and wind effect used in this simulator is presented in the paper. A description of three simulated container ships with similar aerodynamic characteristics is given for the various heading angle and velocity of the wind before the start of the turn. In some experiments, the rudder angle is changed. The results of the simulation are presented in tabular and graphical forms. Quantitative estimates of the displacement of the turning points on the circulation by 90°, 180°, 270° and 360° at wind influence from the corresponding points of circulation in the absence of wind. The graphical dependences of these displacements on the heading angle of the wind and their analytical approximations are presented. The ranges of the wind heading angles at which the maximum effect of the wind on the circulation elements is observed are established. Quantitative estimates of the inversely proportional dependence of the circulation elements on the ship length are calculated. The hypotheses of the other authors about the direction of displacement of the circulation curve under wind action are tested. To verify the validity and reliability of the results obtained, it is necessary to perform similar studies using other mathematical models and natural experiments.

Experimental study on hydro- thermal behavior of journal bearing oil film profile in a slow speed diesel engine

Utilizing universal journal bearing test rig, the performance of a heavy-loaded main journal bearing has been investigated based on tracing operational behavior of oil film temperature and pressure profiles. Test trials were conducted observing the loading program of slow speed diesel engine. Examining the effect of different oil grades, it turned out that oil grade of higher viscosity index enhances journal bearing performance and maintains oil film temperature profile stability at both maximum speeds and heavy loads. Further, medium viscosity grade oil reduces fuel consumption and costs from power production for all operational conditions. Regarding the impact of heavy loads on shaft power performance with medium viscosity oil grade, the differences between optimal and heavy loads get lower as the shaft speed increases. Those were 22 %, 9.7 % and 2.1 % corresponding to the shaft speeds of 43 rpm, 90 rpm and 104 rpm respectively. Additionally, the lower the value of the maximum oil film pressure Pmax difference between optimal and heavy load, the higher the ability of the lubricant to promote performance and provide enhanced operating condition stability. Yet, at part load operating conditions entailing running journal shaft at slow speeds during ship maneuverability, it cannot be considered as the best selection due to the heavy loads often applied in such critical operating conditions. Finally, the test-trial based experimental outcomes can provide useful guidance regarding the proper selection of a lubricant for enhanced performance, cost saving and prolonged journal bearing working life.

Path-following control problem for maritime autonomous surface ships (MASS) in adverse weather conditions at low speeds

With the increasing popularity of autonomous vehicles, the maritime industry has focused on the development of Maritime Autonomous Surface Ships (MASS). An essential aspect of autonomous ships is their ability to follow a predetermined path at sea, as deviations from this path can jeopardize navigational safety, leading to accidents like collisions and grounding incidents. Therefore, ensuring that autonomous ships can safely navigate by following predefined routes is of utmost importance. Simultaneously, the implementation of the Energy Efficiency Design Index (EEDI) by the International Maritime Organization (IMO) has raised interest in a ship's manoeuvring performance in adverse sea conditions, aiming to reduce greenhouse gas emissions. The Marine Environment Protection Committee (MEPC) has provided guidelines to determine the minimum propulsion power required to maintain ship manoeuvrability in adverse conditions. This study investigates the path-following performance of a ship operating at low forward speeds in adverse weather conditions using a free-running Computational Fluid Dynamics (CFD) model, enabling accurate prediction of manoeuvring behaviour. The numerical results emphasise the significance of low forward speeds in enabling a ship to follow a predetermined route, offering valuable insights into path-following performance with minimum propulsion power in adverse weather. The study reveals that increasing propulsive power reduces deviations from the predetermined route when the ship encounters bow and beam waves. However, the impact of propulsion power on deviation is negligible in quartering waves during path-following control. This research contributes to improving guidelines for minimum ship powering, ensuring safe autonomous navigation in adverse weather conditions.

State-of-the-Art Review and Future Perspectives on Maneuvering Modeling for Automatic Ship Berthing

Automatic berthing is at the top level of ship autonomy; it is unwise and hasty to hand over the control initiative to the controller and the algorithm without the foundation of the maneuvering model. The berthing maneuver model predicts the ship responses to the steerage and external disturbances, and provides a foundation for the control algorithm. The modular MMG model is widely adopted in ship maneuverability studies. However, there are two ambiguous questions on berthing maneuver modeling: What are the similarities and differences between the conventional MMG maneuvering model and automatic berthing maneuvering model? How can an accurate automatic berthing maneuvering model be established? To answer these two questions, this paper firstly performs bibliometric analysis on automatic berthing, to discover the hot issues and emphasize the significance of maneuver modeling. It then demonstrates the similarities and differences between the conventional MMG maneuvering model and the automatic berthing maneuvering model. Furthermore, the berthing maneuver specifications and modeling procedures are explained in terms of the hydrodynamic forces on the hull, four-quadrant propulsion and steerage performances, external disturbances, and auxiliary devices. The conclusions of this work provide references for ship berthing mathematical modeling, auxiliary device utilization, berthing aid system improvement, and automatic berthing control studies.

Numerical Investigation of Global Ice Loads of Maneuvering Captive Motion in Ice Floe Fields

During escort and convoy operations, icebreakers are often required to maneuver to open up channels or adjust routes due to the prevalence of ice floe conditions in Arctic routes. This study aimed to investigate the global ice load characteristics of the maneuvering captive motions, including constant turning motion, pure yaw motion, and pure sway motion, of the icebreaker Xue Long, using a combination of the discrete element method (DEM) and drag model. First, the method was verified using simulating Araon model tests from the Korea Institute of Ocean Science and Technology (KIOST). In addition, the maneuvering captive motions of the Xue Long model were simulated at varying turning radii, drift angles, and sway and yaw periods, which are typical but currently poorly studied maneuvering motions. Overall, the results of the study showed that the method is able to reproduce the coupling effect of the ship–ice–water system by considering ship–ice interaction and ice resistance, where the mean deviation and maximum deviation of ice resistance are 9.45% and 13.3%, respectively. The influences of the turning radius, drift angle, and sway and yaw period on the ice resistance and transverse force characteristics were studied and analyzed via ship–ice interactions. The present study provides a prediction tool for the assessment of ship maneuvering performance to assist the hull line development and model testing of icebreakers.

Novel Adaptive Event-Triggered Fuzzy Command Filter Control for Slowly Switched Nonlinear Systems With Constraints.

This article addresses the adaptive fuzzy control problem for switched nonlinear systems with state constraints. The unified barrier function (UBF) is introduced to solve the time-varying state constraints, which removes the feasibility conditions. By integrating command filter into backstepping control to avoid the "explosion of complexity." In addition, a novel event-triggered strategy is designed to deal with the asynchronous switching between subsystems and controllers without limiting the maximum asynchronous time, and mitigate the communication burden. Also, a new threshold function is introduced to overcome the difficulty of discontinuous triggering error at the switching instants. Then, by combining the improved admissible edge-dependent average dwell-time (AED-ADT) method with Lyapunov stability analysis, it is proved that all system signals are bounded and do not violate the predefined constraints under given switching rule. Finally, the numerical simulation results verify the superiority of the proposed algorithm, and the algorithm is applied to a ship maneuvering system.

Estimation of steady wave forces and moment acting on an obliquely moving ship in short waves, and its application in a manoeuvring simulation

In this study, we propose a prediction method for steady wave forces acting on an obliquely moving ship in short waves. Particularly, we enhance an existing method that focuses on refraction of flow and waves in the vicinity of ship's waterline, in terms of the wave amplitude in the near region of the ship and the flow velocity. The former is theoretically modified by deriving an advection equation for wave energy on a ship-fixed frame, and the latter is improved by reflecting the results of computational fluid dynamics that automatically includes viscous effects. A comparison between the prediction and published captive model test results shows that the present method can more adequately capture the variation of the steady wave forces owing to the ship advancing speed and lateral drift than the original method, although the prediction accuracy is not necessarily improved under all conditions. To confirm the applicability of the present method for steady wave forces to ship manoeuvring problems, we predict course-keeping manoeuvres of a ship in regular short waves through a numerical simulation that considers only the steady wave forces in the equation of motion in calm water. The manoeuvring simulation results are validated using previously published free-running model test results, involving various rudder effectiveness conditions for the ship model and full-scale ships, both with and without engine limits. The comparison shows that the present method for steady wave forces is effective in predicting the course-keeping manoeuvres under rudder effectiveness conditions for a ship model in regular short waves. Nonetheless, the present method is insufficiently practical in terms of the manoeuvring simulation under rudder effectiveness conditions for a full-scale ship that is relatively more susceptible to waves. We confirm that an empirical correction for the computed steady wave surge forces based on the discrepancy between the measured and computed values under the ship resting enables the approximate prediction of the course-keeping manoeuvres even under the rudder effectiveness conditions for the full-scale ship.

Research on Ship Trajectory Prediction Method Based on Difference Long Short-Term Memory

This study proposes a solution to the problem of inaccurate and time-consuming ship trajectory prediction caused by frequent ship maneuvering in complex waterways. The proposed solution is a ship trajectory prediction model that uses a difference long short-term memory neural network (D-LSTM). To improve prediction performance and reduce time dependence, the model combines the other variables of dynamic time features in the ship’s Automatic Identification System (AIS) data with nonlinear elements in the sequence data. The effectiveness of this method is demonstrated by comparing its accuracy to other commonly used time series modeling techniques. The results show that the proposed model significantly reduces training time and improves prediction accuracy.

Maneuverability indices estimation of nonlinear response model based on full-scale trial data and square root unscented Kalman filter

Research on ship maneuvering performance provides a theoretical reference for autonomous ship navigation control. The gain and time coefficients accurately describe the dynamic performance of the ship. Given the challenge that the unscented Kalman filter cannot ensure the nonnegativity of the state covariance matrix during the filtering process, this study proposed a parameter identification scheme for a second-order nonlinear response model based on the square root unscented Kalman filter. First, the maneuverability indices of the free-running model were identified based on simulation data. Thereafter, the feasibility and convergence performance of the proposed scheme were verified. Based on full-scale trial data with uncertainty interference, the parameters of the second-order nonlinear response model were estimated for the YUKUN ship. The feasibility, effectiveness, and generalizability of the proposed scheme were further verified. The proposed scheme was analyzed based on three perspectives: parameter convergence, estimation results of the maneuverability indices, and yaw angle prediction results. The results obtained using the proposed algorithm were compared with those obtained using the extended Kalman filter. Finally, the proposed scheme can provide a reference for online parameter identification and prior knowledge for course keeping controllers and autonomous navigation control.

Assessment of Different Hybrid Methods for Simulations of Free Running Surface Ship Maneuvering in Waves

Assessment of Different Hybrid Methods for Simulations of Free Running Surface Ship Maneuvering in Waves

Time-optimal path planning and tracking based on nonlinear model predictive control and its application on automatic berthing

Autonomous shipping has been identified as the way forward in maritime transport. There are numerous technologies being developed over the past decades for maritime autonomous surface ships, including advanced automatic manoeuvring control. As one of the essential indices to evaluate ships’ manoeuvrability, the time-optimal problem remains a challenging issue in the process of path planning and path tracking, which has not been properly addressed for the maritime autonomous surface ships. To fill this knowledge gap, we propose a novel time-optimal path planning and tracking control method based on nonlinear model predictive control (MPC) and spatial reformulation. The proposed time-optimal controller is designed as a two-level hierarchical controller to plan the time-optimal path in spatial coordinates and track it in temporal coordinates when considering the low-speed manoeuvring model. At the high level, the nonlinear MPC based on a spatial formulation is used to generate a planned path of minimum manoeuvring time for a manoeuvring ship. At the low level, the optimal planning trajectory is interpolated by a constant time iteration and the vessel tracks the time-optimal planning trajectory by implementing the nonlinear MPC based on a temporal formulation. As an advanced predictive controller, the nonlinear MPC optimises the berthing path to a minimum time mathematically at the planning stage and compensates for the uncertain disturbance during the tracking process. To validate the time-optimal control method, a typical scenario of low-speed berthing operation is analysed. The results of the case study show that the proposed control method can be successfully applied to time-optimal manoeuvring problems.

Kinematic motion models based vessel state estimation to support advanced ship predictors

Advanced ship predictors can generally be considered as a vital part of the decision-making process of autonomous ships in the future, where the information on vessel maneuvering behavior can be used as the source of information to estimate current vessel motions and predict future behavior precisely. As a result, the navigation safety of autonomous vessels can be improved. In this paper, vessel maneuvering behavior consists of continuous-time system states of two kinematic motion models—the Curvilinear Motion Model (CMM) and Constant Turn Rate & Acceleration (CTRA) Model. Two state estimation algorithms—the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are implemented on these two models with certain modifications so that they can be compatible with discrete-time measurements. Four scenarios, created by combining different models and algorithms, are implemented using simulated ship maneuvering data from a bridge simulator. These scenarios are then verified through the proposed stability and consistency tests. The simulation results show that the EKF tends to be unstable combined with the CMM. The estimates from the other three scenarios can generally be considered more stable and consistent, unless sudden actions or variations in vessel heading occurred during the simulation. The CTRA is also proven to be more robust compared to the CMM. As a result, a suitable combination of mathematical models and estimation filters can be considered to support advanced ship predictors in future ship navigation.

Stochastic stabilization and destabilization of ship maneuvering motion by multiplicative noise

Stochastic stabilization and destabilization of ship maneuvering motion by multiplicative noise

Sparse Bayesian Relevance Vector Machine Identification Modeling and Its Application to Ship Maneuvering Motion Prediction

In order to establish a sparse and accurate ship motion prediction model, a novel Bayesian probability prediction model based on relevance vector machine (RVM) was proposed for nonparametric modeling. The sparsity, effectiveness, and generalization of RVM were verified from two aspects: (1) the processed Sinc function dataset, and (2) the tank test dataset of the KRISO container ship (KCS) model. The KCS was taken as the main research plant, and the motion prediction models of KCS were obtained. The ε-support vector regression and υ-support vector regression were taken as the compared algorithms. The sparsity, effectiveness, and generalization of the three algorithms were analyzed. According to the trained prediction models of the three algorithms, the number of relevance vectors was compared with the number of support vectors. From the prediction results of the Sinc function and tank test datasets, the highest percentage of relevance vectors in the trained sample was below 17%. The final prediction results indicated that the proposed nonparametric models had good prediction performance. They could ensure good sparsity while ensuring high prediction accuracy. Compared with the SVR, the prediction accuracy can be improved by more than 14.04%, and the time consumption was also relatively lower. A training model with good sparsity can reduce prediction time. This is essential for the online prediction of ship motion.

Time series prediction of ship maneuvering motion based on dynamic mode decomposition

In order to reveal the dynamic characteristics and achieve rapid time series prediction of ship maneuvering motion, a reduced-order dynamic mode decomposition (DMD) algorithm is applied to reconstruct and predict the zig-zag and turning circle maneuvering motions. A case study is conducted for a KVLCC2 tanker using its free-running model test data. First, all DMD modes are extracted from the test data, and the dominant DMD modes are selected according to their contributions to the dynamical systems of ship maneuvering motions. Then, the dynamical systems of ship maneuvering motions are reconstructed and predicted by the reduced-order and full-order DMD algorithms. A comparison between reduced-order, full-order DMD algorithms and Gaussian process regression (GPR) is conducted. The dynamic characteristics of the dynamical systems are revealed according to the growth rates and frequencies of the dominant DMD modes. The effects of the truncation rank and input samples are analyzed by a parametric study, which indicates that the truncation rank and input samples are crucial to the prediction accuracy. Besides, the computational time of the different algorithms is compared and analyzed.