Automatic Berthing Research Articles

Consideration of Simulation Scenarios for Safety Assessment of Heading, Speed and Track Control Function

Abstract MASS (Maritime Autonomous Surface Ship) has been under active development in recent years. Especially, a great deal of attention has been paid to the development of the system which automates the navigation task of a ship including situation awareness function, collision and grounding avoidance function and heading, speed and track control function. When considering a MASS for coastal or open sea voyages, verifying the heading, speed and track control function presents a significant challenge during harbour manoeuvring, because of including automatic berthing and unberthing. Thus, it is essential to establish a verification method through simulation technique. While numerous studies have been conducted to develop control algorithms, few have focused on safety assessment, especially in the creation of simulation scenarios. Therefore, this study proposes a method of creating simulation scenarios to assess the safety of heading, speed and track control function for MASS in harbour manoeuvring. There are two main issues. The first is how to set up the passage plan to be included in the scenario, and the second is how to provide environment conditions. To solve the initial issue, a classification of navigational modes is defined, and the possible manoeuvring patterns that can be performed in each mode are organised. To solve the second issue, the method of assigning wind speed and direction to the scenario is examined based on regulations and observational data. Finally, an illustrative scenario is presented to demonstrate the proposed method.

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.

Research on Ship Automatic Berthing Algorithm Based on Flow Matching and Velocity Matching

Addressing the automatic berthing task for vessels, this study introduces the Flow Matching Double Section Bezier Berth Method (FM-DSB) for handling downstream and upstream berthing instructions. By considering the orientation relationship between the direction of water flow and the berth, combined with berthing modes, the algorithm determines the vessel’s entry angle into the berth and plans the berthing path using double-section Bezier curves. Effective control of vessel speed post-path determination is essential. Therefore, based on the response of vessels to propeller inputs, this study introduces the Berthing Path Velocity Matching Method (BPVM). The BPVM ensures speed matching along the berthing path through analysis of vessel acceleration and deceleration capabilities. Subsequently, simulation experiments are conducted to validate the planning algorithm for both long-distance and short-distance berthing. Furthermore, the feasibility and effectiveness of the berthing path are verified using a dual-loop path tracker based on the planned results. Experimental outcomes illustrate the adaptability of the proposed algorithm in planning berthing paths that align with vessel motion characteristics, effectively guiding vessels into berths through the designed dual-loop control system.

Collision-free automatic berthing of maritime autonomous surface ships via safety-certified active disturbance rejection control

This paper addresses the automatic berthing of a maritime autonomous surface ship operating in a confined water environment subject to static obstacles, dynamic obstacles, thruster constraints, and space constraints due to shorelines. A safety-certified active disturbance rejection control (ADRC) method is proposed for achieving the automatic berthing task of an MASS in the presence of model uncertainties and ocean disturbances. An extended state observer (ESO) based on a second-order robust exact differentiator (RED) is employed to estimate an extended state vector consisting of internal model uncertainties and external ocean disturbances. With the aid of the RED-based ESO, a nominal ADRC law is designed to achieve the position and heading stabilization. To avoid collisions with static obstacles, dynamic obstacles, and shorelines, input-to-state safe high-order control barrier functions are used to guarantee safety. Optimized control signals are obtained based on a constrained quadratic programming (QP) problem within safety constraints. In order to translate the control signals into the individual thruster command, a constrained QP problem is further used to search for optimized commands in real time. It is proven that the closed-loop automatic berthing system is input-to-state stable. By using the proposed method, the MASS is able to reach the desired position and heading with collision avoidance. Simulation results verify the effectiveness of the proposed safety-certified ADRC method for automatic berthing.

A berthing state estimation pipeline based on 3D point cloud scan-matching and berth line fitting

The accurate estimation of the berthing state is fundamental for ships’ autonomous navigation during berthing processes. However, most existing methods only provide partial perception information, falling short of automatic berthing requirements. To address this issue, this paper presents a berthing state estimation pipeline that only relies on a 3D LiDAR, which can provide multiple types of berthing status information. The method employs the scan-matching algorithm to estimate the ship’s position and attitude. Additionally, the point cloud line-fitting algorithm is used to establish the berth line equation. By integrating the fitted berth line equation with ship motion data, the ship’s berthing state information is calculated. Experimental results demonstrate that, compared to existing methods, the method boasts significantly smaller errors and can provide more accurate and abundant berthing state information. The proposed method is effective and practical for automatic berthing state estimation and further provides key foundations for achieving automatic berthing operations.

Automatic berthing systems: a review on artificial intelligence methods for marine ship

Automatic berthing systems: a review on artificial intelligence methods for marine ship

Path Planning for Automatic Berthing Using Ship-Maneuvering Simulation-Based Deep Reinforcement Learning

Despite receiving much attention from researchers in the field of naval architecture and marine engineering since the early stages of modern shipbuilding, the berthing phase is still one of the biggest challenges in ship maneuvering due to the potential risks involved. Many algorithms have been proposed to solve this problem. This paper proposes a new approach with a path-planning algorithm for automatic berthing tasks using deep reinforcement learning (RL) based on a maneuvering simulation. Unlike the conventional path-planning algorithm using the control theory or an advanced algorithm using deep learning, a state-of-the-art path-planning algorithm based on reinforcement learning automatically learns, explores, and optimizes the path for berthing performance through trial and error. The results of performing the twin delayed deep deterministic policy gradient (TD3) combined with the maneuvering simulation show that the approach can be used to propose a feasible and safe path for high-performing automatic berthing tasks.

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.

Development of trajectory-tracking maneuvering system for automatic berthing/unberthing based on double deep Q-network and experimental validation with an actual large ferry

The development of an automatic berthing and unberthing system is essential for realizing autonomous ship navigation systems. Although numerous berthing/unberthing methods using neural networks (NNs) have been proposed in recent years, whether NNs trained via numerical simulations including modeling errors can be used for the automatic berthing/unberthing of actual ships in natural external disturbances is not clear. Hence, in this study, a maneuvering system for automatic berthing/unberthing was developed using a NN that solves the trajectory-tracking problem. The proposed method considered future targets as discrete points in learning, such as to absorb the modeling error and reduce the influence of external disturbances; thus, it achieved a more robust control compared to path planning methods. The system achieved trajectory tracking at low speeds by offline learning without teaching data to follow a target ship with the same maneuverability as its own ship and operated randomly under external disturbances. Furthermore, an online target correction logic function that locally corrects targets was developed to increase the tracking ability in external disturbances. Subsequently, a berthing and unberthing experiment was conducted using an actual large ferry to ensure the applicability of simulation-based NNs to the trajectory-tracking control of an actual ship in a real-life environment.

EMPMR berthing scheme: A novel event-triggered motion planning and motion replanning scheme for unmanned surface vessels

In this study, the issues related to approaching and berthing of unmanned surface vessels (USVs) are investigated. The automatic berthing task is divided into motion planning and motion replanning. First, a smooth path from the initial position to the berth is planned using A* algorithm and B-spline. Then, the segmented interpolation method is employed to let the USV velocities converge slowly to zero at the berth, and an initial berthing trajectory is obtained. Moreover, considering the kinetic constraints of the USV, the nonlinear model predictive control (NMPC) algorithm is employed in the offline motion planning and online motion replanning scheme. In this scheme, novel static, and COLREGs-based dynamic obstacle avoidance functions are proposed based on Gaussian functions. Furthermore, the event-triggered mechanism is adopted to improve the real-time performance of the NMPC algorithm. The proposed event-triggered motion planning and motion replanning (EMPMR) scheme and Han et al.’s scheme are tested in six scenarios. Simulation results show that the proposed EMPMR scheme can accomplish USV’s approaching and automatic berthing of USVs and has better obstacle avoidance and computational performance.

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.

An overview of developments and challenges for unmanned surface vehicle autonomous berthing

With the continuous progress of contemporary science and technology and the increasing requirements for marine vehicles in various fields, the intelligence and automation of ships have become a general trend. The autonomous control of surface Unmanned Surface Vessel (USV) generally covers the USV path planning, path tracking control, and autonomous collision avoidance control. But in the whole navigation process of USV, autonomous berthing is also a crucial part. And the research on the algorithm of the automatic berthing process of the USV is less. Mature USV autonomous berthing technology can effectively reduce the cost of human and material resources and financial resources while reducing the accident rate reasonably and safely. Therefore, it is of great importance to comprehensively promote the development of USV autonomous berthing technology.

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.

Safety-Certified Constrained Control of Maritime Autonomous Surface Ships for Automatic Berthing

The berthing of maritime autonomous surface ships (MASSs) is a challenging operation even for an experienced captain due to the required complicated maneuvering at low speeds in a constrained water space. This paper addresses safety-aware automatic berthing of MASSs moving in a constrained water region with stationary and moving obstacles. A safety-critical constrained anti-disturbance control method is proposed for the automatic berthing of MASSs subject to velocity constraints, input constraints, and collision-avoidance constraints, as well as ocean disturbances. At the kinematic level, desired linear velocities and yaw rate are prescribed by incorporating a line-of-sight guidance scheme to achieve position-heading stabilization. At the kinetic level, an anti-disturbance kinetic control law is designed based on an extended state observer. Through converting the input constraints to velocity constraints on guidance signals, the safety-guidance signals are obtained by solving a quadratic programming problem subject to the state, input, and collision-avoidance constraints. It is proven that an MASS controlled by using the proposed method is input-to-state safe in the presence of ocean disturbances. Simulation results are elaborated to substantiate the efficacy of the proposed safety-certified control law for the automatic berthing of an MASS subject to physical constraints and environmental disturbances.

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

An event-triggered trajectory planning and tracking scheme for automatic berthing of unmanned surface vessel

This study proposes a trajectory planning and tracking scheme for the fully actuated USV's automatic berthing. First, a three-degree-of-freedom model (3-DOF) of USV is employed, considering model uncertainty, actuator saturation, and environmental disturbances. Moreover, an A* algorithm with the Bezier curve (AB) approach is proposed to plan the smooth berthing trajectory. To make the velocity of the USV converge to zero at the berth, an interpolation method is proposed to densify the waypoints at the end of the berthing trajectory. Most of the previous studies about USV's tracking control pay little attention to real-time performance, which may increase the computational cost of the control system and affect its stability. Therefore, to improve the computational performance during the USV's berthing, an event-triggered adaptive horizon model predictive control (ETAHMPC) method is proposed. Comparative simulation experiments are performed in two scenarios to verify the effectiveness and superiority of the proposed ETAHMPC scheme. The results show that the proposed scheme can effectively reduce computational costs while ensuring control performance.

Application of Reinforcement Learning to Generate Non-linear Optimal Feedback Controller for Ship's Automatic Berthing System

In this paper, we present an application of reinforcement learning for automatic ship's berthing system. In the proposed method, the model-based reinforcement learning is used to generate non-linear feedback controller. The proposed system is composed of an Actor-Critic algorithm suitable for continuous state and a function approximator by Radial Basis Function networks. To evaluate the performance of the proposed system, extensive computer simulations and actual sea tests are carried out using small training ship Shioji-Maru III under various conditions. As a result, we can see that the proposed non-linear optimal feedback controller by reinforcement learning is useful for designing automatic berthing system for the ship.

Consumption-reduced manual and automatic manoeuvring with conventional vessels

Conventional ships are rarely the focus of research projects on autonomous shipping. Nevertheless, the shipping companies are very interested in reducing fuel consumption. This paper proposes a gradual approach towards ship automation which starts with the intelligent assistance of common manual steering by means of a Maneuver Assistance System (MAS). The MAS displays a manually improved manoeuvre plan and the future motion based on the current actuator commands. These two assistance functions contribute to a more conscious use of actuators which can significantly reduce power consumption. For further automation, the improved manoeuvre plan is converted into a trajectory by forward simulation, which is then used by a control system. The control system includes feed-forward and feedback control as well as an allocation system based on a simplified dynamic motion model. A cascaded structure is used, where an outer track controller provides velocity and heading commands to an inner velocity and heading controller. The paper presents the necessary framework and the application of this approach to the digitised German research vessel DENEB with the aim to gradually introduce and realise automatic manoeuvring. Results from manual and assisted manoeuvres with the DENEB are analysed and compared. Finally, first results of automatic berthing with the vessel in the port of Rostock are presented.

A potential field-based trajectory planning and tracking approach for automatic berthing and COLREGs-compliant collision avoidance

A potential field-based Extended Dynamic Window Approach (EDWA) is proposed to fulfill the real-time trajectory planning for automatic berthing. To avoid falling into a local minimum, an nonuniform Theta* (NT*) is introduced to search the global path considering the risk of obstacles. Three potential fields are established: the attractive potential field guides the unmanned surface vehicle (USV) along its global path; the grid-based repulsive potential field is designed to keep the USV away from shores; and the COLREGs-compliant repulsive potential field is to avoid collision from other USVs. Taking the dynamic constraints of the USV into account, EDWA generates predicted trajectories of both constant force and deceleration phases. The objective function is subsequently produced to select them optimally according to the established potential fields. Finally, a linear MPC is presented to track the designed berthing trajectory considering the real-time requirement. The whole automatic berthing scheme is simulated on the Dalian port. The results indicate that the USVs avoid the potential collisions in accordance with COLREGs, and successfully reach their desired berthing spots with reasonable small deviations.