Ship Maneuvering Research Articles

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.

Turning and zigzag maneuverability investigations on a waterjet-propelled trimaran in calm and wavy water using a direct CFD approach

The evaluation of ship maneuverability characteristics typically involves the mode tests, direct CFD computations, and system-based simulations. The three methods have been widely used in traditional vessels. However, the trimarans, which are appended with the waterjet propulsion systems, have been rarely researched and assessed comprehensively based on the above methods. This paper used a free-running waterjet-propelled trimaran model test to obtain the constant turning and zigzag maneuvering performance in calm water conditions. By embedding the speed and heading controllers in a CFD program, along with the region moving method, a direct free-running maneuvering CFD model was established on a waterjet-propelled trimaran. Compared to the free-running model tests, the simulation results indicated that an empirical hydrodynamic derivative-based Maneuvering Model Group (MMG) method produced the fastest results but lacked accuracy in simulating a constant turning diameter, making it suitable for fast maneuvering estimation and prediction during the design stage. Conversely, a direct force (moment)-based CFD model required more accurate input estimates to acquire acceptable simulated results. Meanwhile, a body force-based CFD model considered more complex nonlinear impacts and was most similar to reality, achieving better agreement with the experimental results. Additionally, the simulations of the direct free-running maneuvering CFD model in calm and wavy water proved advantageous in examining hull-propulsor interaction during physically similar maneuvering processes where the model test was inadequate.

Improved PER-DDPG based nonparametric modeling of ship dynamics with uncertainty

This study contributes to addressing the challenge of quickly obtaining an effective and accurate nonparametric model for describing ship maneuvering motion in three degrees of freedom (3-DOF). To achieve this, an intelligent ship dynamics nonparametric modeling method named improved PER-DDPG is proposed. This method leverages the deep deterministic policy gradient algorithm (DDPG) and prioritized experience replay mechanism (PER) and analyzes the characteristics between the goal of deep reinforcement learning (DRL) and the modeling process of the nonparametric model. The PER mechanism is utilized to enhance the agent’s understanding of the overall mechanism of ship motion by improving the utilization of samples. The meaning of target value is redefined due to transforming DRL aiming at maximizing cumulative rewards into maximizing the set of immediate rewards at each time step. To validate the performance of the proposed modeling method, we conduct simulation studies using a benchmark ship model i.e., a Mariner cargo ship dynamic model, and experimental studies using a real unmanned surface vehicle (USV). In the simulation test, we demonstrate the effectiveness and generalization of the proposed method through zigzag and turning circle tests. Furthermore, we verify the robustness and applicability of the proposed method by using datasets with uncertain environmental disturbances and datasets with different sampling frequencies. Additionally, the experimental tests conducted on the USV indicate the consistency of the proposed approach.

Numerical study on wave-induced drift forces and motions of a DTC ship in head and oblique waves using functional decomposition model

It is imperative to investigate the adequacy of propulsion power and steering mechanisms in order to maintain the manoeuvrability of ships in adverse wave conditions, as the drift forces caused by wave can result in deviation from the desired course. The primary objective of this research is to develop a hybrid approach, combining potential and viscous flows, known as SWENSE (Spectral Wave Explicit Navier-Stokes Equations), to accurately assess the wave-induced drift forces and motions experienced by ships in waves. SWENSE is a hybrid approach based on the functional decomposition model, in which the total field is composed of the incident field and complementary field. In this study, the incident filed waves are calculated by the Airy wave theory and the complementary field is solved by the SWENSE model. The wave conditions are selected according to the benchmark cases of the European Union research project SHOPERA (Energy Efficient Safe Ship Operation). The wave-induced loads and motions of DTC in head and oblique waves are calculated. Results of CFD (Computational Fluid Dynamics) are compared with those of EFD (Experimental Fluid Dynamics) and other numerical methods. In addition, the wave-induced drift forces of the fixed/3DOF (Degrees of Freedom) ships are compared with each other (3DOF includes heave, roll and pitch). SWENSE model is proven to be reliable for the predictions of the wave-induced drift forces and motions of ships in waves.

Numerical PMM test in shallow water using CFD method

This study aims to the estimation of the ship manoeuvrability in shallow water by using the numerical PMM approach, which analyses the hull hydrodynamic responses and manoeuvring performance from the viewpoint of coefficients. The test model is a scaled KVLCC2 tanker operating at a low Froude number of 0.0514. Static drift test and pure yaw test are considered, and the water depths vary from deep water to very shallow water of 20% under-keel clearance. The numerical tank is built based on an OpenFOAM+ package. The free surface is modelled by volume-of-fraction method, and the k-ω SST model associating with wall functions is applied for turbulence. After the convergence tests and validation works, the numerical tank is employed to investigate the wave-depth effect on hydrodynamic forces and manoeuvring coefficients. The present results show that the forces of static drift test, particularly sway force, can be significantly changed as the water depth decreases.

Generation of naturalistic and adversarial sailing environment (NASE) for intelligent test of autonomous ships

—Intelligence testing and verification of autonomous sailing capability is crucial to the development and deployment of autonomous ships. The prevailing approach is to establish a real test site on the sea or create a life-like simulator to test the autonomous sailing functions. However, due to the high dimensionality of the naturalistic sailing environment and the rareness of encounter events, hundreds of millions of miles and long test cycles would be required to demonstrate the collision avoidance performance of autonomous ships, which results in huge consumption. A novel approach is presented to generate the naturalistic and adversarial sailing environment by employing the sparse but adversarial adjustments into the naturalistic sailing environment. Our method can be used to enhance the existing sailing environment simulations to accelerate the autonomous ship test process. Firstly, the naturalistic probabilistic distributions of ship sailing behaviors are analyzed. Then, the naturalistic sailing environment (NSE) is generated to achieve real-world renderings. Moreover, the characteristics of ship maneuvering behavior at different encountered situations are identified. Furthermore, the naturalistic and adversarial sailing environment (NASE) is established by sampling maneuvers of the background ships (BS) from the importance functions at critical moments. It ensures the authenticity of the scenarios and increases the generation probability of encounter scenarios. The effectiveness of the proposed naturalistic and adversarial sailing environment model in the simulation of narrow waterway environment is proved. Compared with the naturalistic sailing environment, the proposed model can accelerate the generation speed of the critical scenarios and improve the generation efficiency of encounter scenarios.

TwinPort: 5G drone-assisted data collection with digital twin for smart seaports

Numerous ports worldwide are adopting automation to boost productivity and modernize their operations. At this point, smart ports become a more important paradigm for handling increasing cargo volumes and increasing operational efficiency. In fact, as ports become more congested and cargo volumes increase, the need for accurate navigation through seaports is more pronounced to avoid collisions and the resulting consequences. To this end, digital twin (DT) technology in the fifth-generation (5G) networks and drone-assisted data collection can be combined to provide precise ship maneuvering. In this paper, we propose a DT model using drone-assisted data collection architecture, called TwinPort, to offer a comprehensive port management system for smart seaports. We also present a recommendation engine to ensure accurate ship navigation within a smart port during the docking process. The experimental results reveal that our solution improves the trajectory performance by approaching the desired shortest path. Moreover, our solution supports significantly reducing financial costs and protecting the environment by reducing fuel consumption.

Real-time control of ship's roll motion with gyrostabilisers

The safety of ship manoeuvring operations is greatly affected by large roll motions under severe wave conditions. While existing studies have focused on optimizing hydrodynamic performance devices, these approaches may not be effective under realistic sea conditions where wave frequency and direction are irregular. Thus, predicting ship motion tendencies and implementing a reasonable control approach is crucial for improving ship safety. This study proposes a novel and practical control strategy that can effectively suppress roll motion of ships under realistic random wave conditions. The approach utilizes a real-time controller based on model predictive control (MPC) that is actuated by a dual gyrostabilizer. The ship's roll motion response in beam waves is calculated using one degree-of-freedom hydrodynamic modeling, and the wave memory effect is taken into consideration by employing system identification. A synthetic mathematical model of MPC and the dual gyrostabilizer method is introduced in state-space representation to compensate for wave excitation moment in real-time. The proposed method's efficacy is validated by comparing with a roll stabilization scenario of a particular ship in the literature, and control performance of a standard ship under regular and irregular wave conditions is separately discussed. Results indicate that the proposed method can significantly reduce roll motion amplitude and is feasible for nonlinear ship motion control problems. This study sheds light on the multi-degree-of-freedom motion control of ships under beam wave conditions.

Analysis and Exploration of the Impact of Average Sea Level Change on Navigational Safety in Ports

The primary clientele of a harbor is vessels, and vessels are primarily influenced by external forces such as wind (on the water surface), currents (underwater), and waves (affecting vessel stability). Therefore, it is necessary to comprehensively consider safety factors such as marine environmental forces and port characteristics. As ship sailing falls under applied science, acquiring marine meteorological information regarding ship routes can enhance port navigational safety. However, in the face of changes in the environmental conditions of harbor waters, it is essential to fully consider the impact of the external environment on ship maneuvering. One can effectively navigate complex operating environments by devising reasonable ship-handling plans. In the context of sea level rise caused by extreme climatic events, long-term variations, trends, and random factors are at play. Previous assessments of sea level rise have often relied on linear regression and the least squares method to determine coefficients. However, these methods fail to accurately capture the actual trend of sea level rise. Additionally, traditional harmonic analysis methods are unable to analyze sea level rise as well. Therefore, in this study, the techniques of simple moving average (SMA), empirical mode decomposition (EMD), and ensemble empirical mode decomposition (EEMD) were applied to analyze sea level rise. The obtained results of sea level rise under different analysis conditions were integrated with a hydrodynamic model that incorporates both wave and tidal characteristics to calculate the overall coastal dynamics parameters, which are crucial for ship navigation. The research findings contribute to the study of ship navigational safety issues by examining the distribution characteristics of port meteorology under climate change conditions. They offer valuable insights for mariners to assess navigational safety and devise maneuvering strategies based on the actual water flow conditions. Furthermore, the findings help identify and address potential risks and issues, ultimately ensuring the safety of navigation.

A COLREGs-Compliant Ship Collision Avoidance Decision-Making Support Scheme Based on Improved APF and NMPC

In this paper, combined with the improved artificial potential field (IAPF) method and the nonlinear model predictive control (NMPC) algorithm, a collision avoidance decision-making support scheme considering ship maneuverability and the International Regulations for Preventing Collisions at Sea (COLREGs) is proposed. First, to comply with the requirements of COLREGs, an improved repulsive potential field is presented for different encounter scenarios when the ship detects the risk of collision, and the coordinated ship domain is applied to provide safety criteria for collision avoidance. Then, by transforming the MMG model to a discrete-time nonlinear system, the NMPC is utilized to predict the future state of the ship according to the current state, and the IAPF method is incorporated to calculate the potential field in each future state as the objective function. Following this approach, the action taken to avoid collision is more effective, the ship motion in avoiding collision is more accurate, and the collision avoidance decision making is more reasonable. Finally, two simulation examples of multi-ship encounter scenarios are applied to illustrate the merits and effectiveness of the proposed collision avoidance decision-making support scheme.

Nonparametric modeling of ship maneuvering motions in calm water and regular waves based on R-LSTM hybrid method

It is essential to establish reliable method to predict the ship maneuvering motions in complex navigation environments for navigation efficiency and safety. In this paper, a novel hybrid nonparametric modeling method (R-LSTM) is developed by coupling Bidirectional Long Short-Term Memory (Bi-LSTM) with residual network and attention mechanism. Firstly, the prediction capability of the proposed hybrid method is evaluated in the predictions of zigzag maneuvers in calm water. The model test data of a KVLCC2 tanker model are utilized for modeling and validation, and the optimal parameters are determined by Tree-structured Parzen Estimator algorithm. The results demonstrate that the proposed method has higher prediction accuracy and better generalization ability than the classical LSTM method. On this basis, the online modeling method for predicting the turning motion in regular waves is developed along with sliding time window and time series split cross-validation. The model test data of a KCS container ship and a ONRT tumblehome ship under different wave conditions are used for online modeling and real-time prediction. The results indicate that the ship trajectories with trajectory drift and fluctuation features during the turning motion under the wave impacts are captured well by the developed method with satisfactory confidence intervals.

Math-data integrated prediction model for ship maneuvering motion

Highly accurate motion prediction is critical to facilitate safe navigation and autonomous control of a ship. In this paper, a math-data integrated prediction (MDIP) model of ship maneuvering motion is newly developed by combining with mathematical and data-driven modules. The math part is identified by employing variable-order hydrodynamic derivatives which are derived from Taylor expansion. Using extended Kalman filter, an optimal-order mathematical prediction model is obtained. The data-driven part dwells on prediction residuals which are approximated by a least square-support vector machine. Furthermore, integration mechanisms are devised to cohere mathematical and data-driven models as a whole, by deploying summation and neural network approximation, respectively. By comparisons, it is verified that not only order selection but also math-data integration can enhance ship motion prediction accuracy, for which various maneuvering tests are analyzed. The results demonstrate that the proposed MDIP model using math-data integration offers much stronger generalization, thereby paving a new path for ship maneuvering motion prediction.

Prediction of ship maneuvering motion with grey-box modelling incorporating mechanism and data

ABSTRACT A grey-box modelling incorporating mechanism and data is presented in this paper to predict the ship manoeuvering motion. The traditional mathematical model in three degrees of freedom is adopted to reveal the known movement mechanism, in which an additional hydrodynamic correction term is developed by Neural Network (NN) based on the data to adaptively estimate the errors induced by the idealised approximation. The KRISO Container Ship (KCS) is taken as the study object and the free-running model tests are carried out to obtain the data for establishing the NN model of hydrodynamic correction terms. The typical manoeuvers (tactical circles, zigzags, Williamson turns) are simulated by the grey-box model and the mathematical model, respectively. The results indicate that the grey-box modelling method proposed in this paper can not only improve the prediction accuracy but also demonstrate a good generalisation performance.

Ship voyage optimization based on fuel consumption under various operational conditions

Aiming at reducing ship emissions and lower operational cost, less fuel consumption is always pursued by shipping industry. Compared with other methods on reducing the fuel consumption, such as optimizing the design of hull lines or ship propulsion system, setting additional energy saving device, the voyage optimization is a way which is easier to implement and needs less investment cost. In this paper, an integrated simulation model of ship propulsion and maneuvering system is established and verified. Then, a benchmark 38800 t cargo ship and a 32n miles coastal service are selected for the case study. Based on the ship operating characteristic and voyage environments, 24 representative cases of combination about the voyage set up and ship control modes are proposed. The ship fuel consumptions under 24 cases are calculated using the integrated simulation model. Finally, fuel consumption change under different conditions are analyzed and the most reasonable case of saving fuel consumption is provided. This study tries to investigate the influence of multiple factors on the ship fuel consumption considering the realistic and representative operating conditions, and provides theoretical suggestions for ship propulsion and maneuvering control, voyage planning and engine management for reducing fuel consumption over ship voyages.

Optimized Turning Basin Design for Inland Waterways

Concept design guidelines for ship turning basins are often not met in practice due to environmental, economic, or geotechnical restrictions. In these cases, a detailed design procedure is followed to develop an optimized turning basin geometry within the environmental constraints. Then, real-time ship maneuvering simulations are used to evaluate the maneuvers in the proposed geometry. However, such a detailed design procedure is costly, especially if a large number of simulations are required. To restrict the number of simulations, the waterway authority [Voies navigables de France (VNF)] ordered a study to find a generic and optimal design for a turning basin which could be suitable for the upgrade in the northern part of their inland waterway network. Therefore, the turning maneuvers for two typical inland navigation vessels were simulated in real time under different hydrometeorological conditions. This study was conducted in three stages. First, maneuvers in traditional circular and trapezoidal turning basins of different sizes were executed and analyzed. Second, optimized turning basin geometries were developed. Finally, the turning maneuvers in those optimized geometries were analyzed. Based on the results of this analysis, a stepwise approach was proposed to select an optimized turning basin geometry that was based on the conditions present on site. This stepwise approach for the design of turning basins could reduce the real-time simulations that are required when upgrading a waterway network.

Validation of maneuvering simulations for a KCS at different forward speeds using the 4-DOF MMG method

Ships with a high center of gravity and at relatively fast speed, such as container vessels, may experience significant heel during turning at a rudder angle of 35°. Accurate prediction of the maneuvering motion of such ships requires consideration of roll motion in addition to surge, sway, and yaw motions. This study applies the 4-Degrees of Freedom (4-DOF) Maneuvering Modeling Group (MMG) method, developed by one of the authors, as a practical simulation method that includes the roll-coupling effect, to predict the maneuvering of a Kriso Container Ship (KCS). To validate the accuracy of the maneuvering simulations, the results are compared with those obtained from free-running model tests for turning and zig-zag maneuvers. Furthermore, to capture the effect of speed on maneuvering, the free-running tests are conducted by varying the approach speed of the ship model. The test results reveal that the advance in turning and the overshoot angle in zig-zag maneuvers increase with speed, while the tactical diameter decreases. The simulation results obtained using the 4-DOF MMG method are consistent with the free-running test results with practical accuracy. Therefore, the 4-DOF MMG method proves useful in predicting the maneuvering of ships, such as container ships, at fast speeds.

Non-parameterized ship maneuvering model of Deep Neural Networks based on real voyage data-driven

While Deep Neural Network (DNN) models for ship maneuvering model are commonly constructed using experimental model ships or simulation data, this study focuses on training four Non-parameter Ship maneuvering Network models (NSM_Net_models) by using real ship voyage data, then evaluating their performance by comparison. To support the data-driven approach, real ship voyage data is collected from the Voyage Data Recorder (VDR) system of a training ship. Along with this, the input variables of the models are enhanced by incorporating the past movement history of ships. Furthermore, the impact of three different durations of past ship motion history on the models’ performance is compared and analyzed. The results suggest the feasibility and practical application value of the ship maneuvering motion model built by the deep network.