Berthing Control Research Articles

Research on autonomous berthing control of MASS based on real time kinematic

Technologies for autonomous navigation of MASS (Maritime Autonomous Surface Ship) in open waters are advancing rapidly. However, the autonomous berthing technology, which represents the final phase of MASS achieving fully autonomous navigation, is still in the process of maturing. Consequently, the study of autonomous berthing control technology holds significant practical importance for the realization of fully autonomous navigation in MASS. This paper proposes an autonomous berthing control program for MASS, based on a MFAC (Model Free Adaptive Control) algorithm that integrates control technology and perception technology. The program encompasses the design of both the berthing logic algorithm and control algorithm. For the controllers, the Preview Deviation Yaw Control System and Speed Control System are designed using the CFDL-MFAC (Compact Form Dynamic Linearization based MFAC) algorithm. The RTK (Real Time Kinematic) data are utilized as inputs for the controller. The experiments are conducted in Lingshui Bay, Dalian, China. The results demonstrate that the lateral distance between the vessel's midship and the berth is less than 1.5 times the vessel's breadth, and the approaching angle is within the range of 0° to 10°, which shows that the proposed program could effectively help vessels achieve desirable berthing states.

Automatic berthing of unmanned surface vessels with predetermined performance

To solve the problem of ship automatic berthing control due to unknown time-varying disturbance and dynamic uncertainty of model parameters, an automatic berthing control law based on predefined performance time function is proposed. First, a predefined performance time function is designed and coupled with tracking error to achieve the predetermined performance of tracking error. Secondly, radial basis function neural network is used to approach the dynamic uncertainty of ship model parameters, and the complex uncertainty of model parameters and unknown time-varying disturbance is represented by linearized parameter form with single virtual parameter, which makes the calculation simple and easy to implement in engineering. On this basis, the reverse step control law is designed. Thirdly, the stability of the system is proved based on Lyapunov stability theory. Finally, the simulation results show that the control law can make the ship reach the desired position and heading angle, and realize the automatic berthing of the ship. The control law and berthing controller designed in this paper have good applicability and robustness, which provides a theoretical basis for the subsequent control research of surface intelligent ships.

Integrated trajectory planning into automatic berthing control of underactuated ship based on fuzzy-backstepping method

The trajectory planning and automatic berthing control are traditionally studied separately; however, the planned trajectory is indeed required to be the reference trajectory for the automatic berthing control system. In this paper, a novel trajectory planning strategy is presented and integrated into the automatic berthing control of underactuated ship. First, a novel trajectory planning strategy, including the Bezier curve planning, the least square fitting, and the speed assignment mechanism, is presented for the first time herein to provide the differentiable reference trajectory for the automatic berthing control system based on backstepping method. Second, a novel coordinate transformation between the Earth-fixed frame and the berth-fixed frame is established for the first time herein for the arbitrary berth position. Third, the fuzzy logic system is incorporated into backstepping controller to alleviate the influence of the model uncertainty, which is probably introduced by the shallow water and bank effects. The closed-loop signals are proved to be stable based on Lyapunov theorem. Two simulations are conducted to illustrate the effectiveness and advantages of the presented fuzzy-backstepping-based automatic berthing control methodology.

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.

Ship Autonomous Berthing Simulation Based on Covariance Matrix Adaptation Evolution Strategy

Existing research on auto-berthing of ships has mainly focused on the design and implementation of controllers for automatic berthing. For the real automatic docking processes, not only do external environmental perturbations need to be taken into account but also motion paths, docking strategies and ship mechanical constraints, which are important influential factors to measure autonomous docking methods. Through a literature review of ship path planning and motion control for automatic berthing, it is found that many studies ignore the interference of the actual navigational environment, especially for ships sailing at slow speed when berthing, or do not consider the physical constraints of the steering gear and the main engine. In this paper, we propose a hybrid approach for autonomous berthing control systems based on a Linear Quadratic Regulator (LQR) and Covariance Matrix Adaptation Evolution Strategy (CMA-ES), which systematically addresses the problems involved in the berthing process, such as path planning, optimal control, adaptive berthing strategies, dynamic environmental perturbations and physically enforced structural constraints. The berthing control system based on the LQR and modified LQR-CMA-ES have been validated by simulation work. The simulation results show that the proposed method is able to achieve the automatic docking of the ship well and the system is robust and well adapted to environmental disturbances at slow speed when docking.

Covariance Matrix Adaptation Evolutionary Strategy with Worst-Case Ranking Approximation for Min–Max Optimization and Its Application to Berthing Control Tasks

In this study, we consider a continuous min–max optimization problem minx∈ 𝕏maxy∈ 𝕐f(x, y) whose objective function is a black-box. We propose a novel approach to minimize the worst-case objective functionF(x) = maxy∈ 𝕐f(x, y) directly using a covariance matrix adaptation evolution strategy in which the rankings of solution candidates are approximated by our proposed worst-case ranking approximation mechanism. We develop two variants of worst-case ranking approximation combined with a covariance matrix adaptation evolution strategy and approximate gradient ascent as numerical solvers for the inner maximization problem. Numerical experiments show that our proposed approach outperforms several existing approaches when the objective function is a smooth strongly convex–concave function and the interaction betweenxandyis strong. We investigate the advantages of the proposed approach for problems where the objective function is not limited to smooth strongly convex–concave functions. The effectiveness of the proposed approach is demonstrated in the robust berthing control problem with uncertainty.

Automatic berthing control under wind disturbances and its implementation in an embedded system

Automatic berthing control under wind disturbances and its implementation in an embedded system

Robust Ship Berthing Control with Wind Disturbance Compensation using Reachability Analysis

Automatic ship berthing under environmental disturbances is considered one of the most challenging control tasks. In particular, wind is a dominant disturbance that greatly impacts ships’ berthing operations. Feedforward control with disturbance observation is a common strategy to compensate for wind effects, however the disturbance observation is limited to mild low-frequency disturbances. Its prediction accuracy and the resultant control performance of wind-effect compensation degrade in severe wind conditions. This study proposes a new control strategy for wind compensation based on the reachable set reflecting the characteristics of the controller and bounded wind disturbance. State feedback linearization is used for berthing control and zonotope sets are employed for reachability analysis. Simulation results are presented to demonstrate the proposed wind compensation method's feasibility and performance.

Automatic berthing using supervised learning and reinforcement learning

Although various studies have been conducted on automatic berthing, including offline optimization and online control, real-time berthing control remains a difficult problem. Online control methods without reference trajectories are promising for real-time berthing control. We used reinforcement learning (RL), which is a type of machine learning, to obtain an online control law without reference trajectories. As online control for automatic berthing is difficult, obtaining an appropriate control law with naive reinforcement learning is difficult. Furthermore, almost all existing online control methods do not consider port geometries. This study proposes a method for obtaining online berthing control laws by combining supervised learning (SL) and RL. We first trained the controller using offline-calculated trajectories and then further trained it using RL. Owing to the SL process, the proposed method can start the RL process with a good control policy. We evaluated the control law performance of the proposed method in a simulation environment that considered port geometries and wind disturbances. The experimental results show that the proposed method can achieve a higher success rate and lower safety risk than the naive SL and RL algorithms.

System parameter exploration of ship maneuvering model for automatic docking/berthing using CMA-ES

Accurate maneuvering estimation is essential to establish autonomous berthing control. The system-based mathematical model is widely used to estimate the ship’s maneuver. Commonly, the system parameters of the mathematical model are obtained by the captive model test (CMT), which is time-consuming to construct an accurate model suitable for complex berthing maneuvers. System identification (SI) is an alternative to constructing the mathematical model. However, SI on the mathematical model of ship’s maneuver has been only conducted on much simpler maneuver: turning and zig-zag. Therefore, this study investigates the SI on a mathematical model capable of berthing maneuver. The main contributions of this study are as follows: (i) construct the system-based mathematical model on berthing by optimizing system parameters with a reduced amount of model tests than the CMT-based scheme; (ii) Find the favorable choice of objective function and type of training data for optimization. Global optimization scheme CMA-ES explored the system parameters of the MMG model from the free-running model’s trajectories. The berthing simulation with the parameters obtained by the proposed method showed better agreement with the free-running model test than parameters obtained by the CMT. Furthermore, the proposed method required fewer data amounts than a CMT-based scheme.

Saddle Point Optimization with Approximate Minimization Oracle and Its Application to Robust Berthing Control

We propose an approach to saddle point optimization relying only on oracles that solve minimization problems approximately. We analyze its convergence property on a strongly convex–concave problem and show its linear convergence toward the global min–max saddle point. Based on the convergence analysis, we develop a heuristic approach to adapt the learning rate. An implementation of the developed approach using the (1+1)-CMA-ES as the minimization oracle, namely, Adversarial-CMA-ES, is shown to outperform several existing approaches on test problems. Numerical evaluation confirms the tightness of the theoretical convergence rate bound as well as the efficiency of the learning rate adaptation mechanism. As an example of real-world problems, the suggested optimization method is applied to automatic berthing control problems under model uncertainties, showing its usefulness in obtaining solutions robust to uncertainty.

On neural network identification for low-speed ship maneuvering model

Several studies on ship maneuvering models have been conducted using captive model tests or computational fluid dynamics (CFD) and physical models, such as the maneuvering modeling group (MMG) model. A new system identification method for generating a low-speed maneuvering model using recurrent neural networks (RNNs) and free running model tests is proposed in this study. We especially focus on a low-speed maneuver such as the final phase in berthing to achieve automatic berthing control. Accurate dynamic modeling with minimum modeling error is highly desired to establish a model-based control system. We propose a new loss function that reduces the effect of the noise included in the training data. Besides, we revealed the following facts—an RNN that ignores the memory before a certain time improved the prediction accuracy compared with the “standard” RNN, and the manual random maneuver test was effective in obtaining an accurate berthing maneuver model. In addition, several low-speed free running model tests were performed for the scale model of the M.V. Esso Osaka. As a result, this paper showed that the proposed method using a neural network model could accurately represent low-speed maneuvering motions.

Path-following Control for Autonomous Navigation of Marine Vessels Considering Disturbances

Path-following control is considered as one of the most fundamental skills to realize autonomous navigation of marine vessels in the ocean. This study addresses with the path-following control for a ship in which there are environmental disturbances in the directions of the surge, sway, and yaw motions. The guiding principle and back-stepping method was utilized to solve the ship’s tracking problem on the reference path generated by a virtual ship. For path-following control, error dynamics is one of the most important skills, and it extends to the research fields of automatic collision avoidance and automatic berthing control. The algorithms for the guiding principles and error variables have been verified by numerical simulation. As a result, most error variables converged to zero values with the controller except for the yaw angle error. One of the most interesting results is that the tracking errors of path-following control between two ships are smaller than the existing safe passing distances considering interaction forces from near passing ships. Moreover, a trade-off between tracking performance and the ship’s safety should be considered for determining the proper control parameters to prevent the destructive failure of actuators such as propellers, fins, and rudders during the path-following of marine vessels.

Research on the Control Strategy and Control Method of Ship’s Automatic Berthing

Ship berthing and maneuvering have become one of the difficult and complex maneuvers. With the development of large-scale, rapid, and automated ships, the navigation safety and efficiency of ships have become more and more important. In this study, the control problem of automatic berthing is studied and analyzed, and the control strategy of automatic berthing is compared and analyzed. This study uses dynamic output feedback control, robust control, neural network control and other methods to automatically berth under-driven ships. In the research of berthing control, a robust adaptive control method is proposed by combining dynamic surface control technology with backstepping method for the automatic berthing control problem of intelligent ships under the influence of environmental disturbance and quay wall effect. This method solves the problem of increased computational complexity caused by the traditional backstepping method for the virtual control derivation, and effectively eliminates the chattering phenomenon of the control output.

Virtual guide automatic berthing control of marine ships based on heuristic dynamic programming iteration method

This paper addresses the berthing control problem for automatic ships by using a virtual guide system based on heuristic dynamic programming (HDP) method. Firstly, by introducing an automatic virtual guide system, the berthing control problem can be transformed into a tracking control problem, and then can be further transformed into an optimal regulation problem. Secondly, the HDP method is used to solve the optimal regulation control problem of the marine surface ship with unknown ship model. Then, it is proven that the tracking error, the adaptation laws and the control inputs are uniformly bounded on the basis of the Lyapunov theory. Finally, simulations are carried out on an automatic model ship and the HDP method is compared with the backstepping control to verify the effectiveness of the developed control scheme.

Artificial Neural Network Controller for Automatic Ship Berthing Using Separate Route

The operation of ships in the port area requires not only the assistance of in-vessel equipment such as main engines and rudder, but also the assistance of external equipment such as tugboats. The complexity in the operation of ships in the port, requires control algorithm with multiple input and output for the automatic berthing control of the ship. The entering and leaving data of the ship can help the algorithm to efficiently control the berthing and un-berthing process of ships. This is based on the artificial intelligence which has been continuously approaching the operating habits of the pilot. The advances in artificial intelligence can control the entering, turning, and berthing in the port by artificial intelligence. In this study, the artificial neural network algorithm has been used to establish an automatic berthing model, based on the scheduled route. With the help of training data of one port, this model can be applied to the ship’s berthing with different berth layouts. Furthermore, it can also be applied to complex systems such as direct or turning-berthing of a ship. Finally, the automatic berthing model has been used for the simulation of direct berthing and turning-berthing in different berth.

Artificial Neural Network controller for automatic ship berthing: challenges and opportunities

Ship berthing is a complicated manoeuvring process that demands precise control of the speed and course of an under-actuated system. As berthing involves low speed running of a ship under environmental disturbances with reduced manoeuvrability, a professional ship handler often faces difficulties in controlling heading in such a situation. To bring automation in ship berthing, most of the researchers have agreed that Artificial Neural Network (ANN) plays a vital role as it has the ability to learn from human experience and replicate similar action in an unknown situation. However, we are still far away from implementing it for real berthing control as we do not have any self-fulfilling ANN controller yet, which can treat all the major issues relevant to a complicated ship berthing operation. Based on contemporary research findings, this paper, therefore, highlights four major challenges that have to be taken into account while proposing an ANN controller for ship berthing, and a comprehensive summary of how to deal with those. The first is how to provide consistent teaching data while training ANN controller to make it more robust; second is how to make the controller universal to do berthing in any port; third is how to tackle the wind disturbances while automation in progress; and the fourth is how to align a ship to the pier, which is the final stage of berthing.

Path following algorithm application to automatic berthing control

This paper aims to verify a new automatic berthing system using a path following algorithm. Berthing operation is one of the most burdensome tasks for crews among several ship operations. The maneuverability of a ship at low speed during berthing operation deteriorates and becomes more vulnerable to disturbances such as wind. Therefore, it is necessary to support and automate operations that require advanced skills such as berthing operation. Previous studies on automatic berthing have investigated various methods to handle the nonlinearity of ship maneuvering motion and determine the optimal control variable. There is a trade-off between accuracy and real-time performance of berthing control from these studies. The algorithms must have sufficiently real-time performance while maintaining the accuracy of control. For these purposes, we propose the automatic berthing system applied a path following algorithm for a ship with one propeller and one rudder in this paper. We show the mathematical model for numerical simulation of berthing control and carried out system identification of the subject ship. In full-scale experiments, the proposed system performed automatic berthing control in both calm wind conditions around 2 m/s and strong wind conditions around 6 m/s.

Consistently Trained Artificial Neural Network for Automatic Ship Berthing Control

In this paper, consistently trained Artificial Neural Network controller for automatic ship berthing is discussed. Minimum time course changing manoeuvre is utilised to ensure such consistency and a new concept named ‘virtual window’ is introduced. Such consistent teaching data are then used to train two separate multi-layered feed forward neural networks for command rudder and propeller revolution output. After proper training, several known and unknown conditions are tested to judge the effectiveness of the proposed controller using Monte Carlo simulations. After getting acceptable percentages of success, the trained networks are implemented for the free running experiment system to judge the network’s real time response for Esso Osaka 3-m model ship. The network’s behaviour during such experiments is also investigated for possible effect of initial conditions as well as wind disturbances. Moreover, since the final goal point of the proposed controller is set at some distance from the actual pier to ensure safety, therefore a study on automatic tug assistance is also discussed for the final alignment of the ship with actual pier.

Distance Measurement System using A Stereo Camera and Radial Pattern Target for Automatic Berthing Control

In this paper, we propose a distance measurement system for automatic berthing control using a stereo camera mounted on a rotation control device, and a radial pattern target. Automatically controlling the position and attitude of a ship aims to prevent maritime accidents due to human error. Our goal is to measure the relative distance between a ship and an onshore or offshore target for berthing. Therefore, the distance should be continuously measured while tracking a fixed point on a target. To this end, we developed a stereo camerabased distance measurement system that satisfied these requirements. This paper describes the structure and principle of the measurement system. We validate the distance error for target incline due to the relative position and attitude between a camera and a target in miniature scale. In addition, the findings of an experiment in an outdoor environment demonstrate that the proposed measurement system has accuracy within 1 m at a range of 20-100 m which is the acceptable accuracy for automatic berthing.