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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.

Autonomous berthing technology is a crucial engineering control problem within the ship intelligence system, encompassing a series of complex operations and technologies. Firstly, this paper analyses the research on autonomous berthing technology from a bibliometric point of view in order to obtain an overview of its past and present development and to outline the importance of this technology. Secondly, a literature review is conducted on each of the four aspects of autonomous berthing technology, namely sensing technology, berthing type, control method, and evaluation method, which can help to quickly understand the main aspects of this technology. Thirdly, the ship-assisting technologies needed to achieve autonomous berthing are discussed and analysed from six aspects: dynamic collision avoidance, path planning, path tracking, heading control, tug assistance, and shore-based systems. Finally, the challenges faced by the ship autonomous berthing technology on the way of development are summarised, and future development is projected. This paper aims to provide a more comprehensive perspective for analysing and researching ship autonomous berthing technology.

Due to environmental considerations, ships are being required to turn off their generators while berthing and receive electric power from the shore. This method of electrical connection of ships to ports during berthing is called cold ironing. Cold ironing can significantly contribute to air quality improvement by mitigating harbours’ CO 2 , nitrogen oxygen and several harmful emissions. This study investigates the needs and assess the first steps of cold ironing implementation in the Port of Heraklion, while also discussing the relative energy mix of the island.

This paper designs an optimal control strategy for the auto‐berthing control (ABC) problem of underactuated surface vessels. The purpose is to achieve accurate berthing of USVs in complex environments. In the traditional ABC problem of surface vessels, the underactuated characteristics of USVs complicate the control design. This underactuated phenomenon is manifested in the lack of lateral driving force on the boat, which restricts its motion to a specific direction. In order to solve the problem of the lack of lateral driving force of USVs, this paper first uses diffeomorphism transformation to transform the original nonholonomic constraint system into a chain structure. Through this transformation, the problem of the state of the vessel being restricted during motion is avoided. On this basis, this paper further describes the optimal control problem of the USV on the horizontal plane based on the kinematic and dynamic models of the USV. In order to solve the optimal control problem, this paper adopts the Gaussian pseudospectral method (GPM). By discretizing the continuous optimal control problem into a nonlinear programming problem, the computational complexity is effectively reduced and the solution efficiency is improved. The optimal navigation trajectory of the USV system and the corresponding optimal control input are then obtained. In order to verify the feasibility and effectiveness of the proposed control strategy, numerical simulation is carried out. The simulation results show that the optimal control strategy can achieve stable and accurate berthing of USVs.

Safe and efficient berthing is essential to ensure maritime transportation and the safety of ships and ports. Three-dimensional (3D) light detection and ranging (LiDAR) can monitor and support ship berthing because it provides abundant target information and offers excellent advantages in measuring accuracy. Hence, a berthing information extraction system has been developed based on 3D LiDAR. Principal component analysis is used to calculate a ship’s heading and the normal vector, and the feature points of the bow and stern are determined. The segments passing through the points are obtained via region growing. The bow and stern are recognized by the similarity of the normal vector of the segments and ship’s heading according to the positions of the ship relative to the berth through visibility analysis. Qualitative and quantitative calculated analyses of the distance, velocity, and approach angle of the dynamic ship’s bow and stern relative to the dock are performed based on the feature points of 3D LiDAR data. A laser scanner, used as the detection unit, efficiently monitored the Ro–Ro ship Ocean Island berthing at Lushun Port in field experiments. On-site applications demonstrated the feasibility and effectiveness of the proposed method for the recognition of dynamic ship target and ensuring safe ship berthing.

SAR Ship Detection Dataset (SSDD) is the first open dataset that is widely used to research state-of-the-art technology of ship detection from Synthetic Aperture Radar (SAR) imagery based on deep learning (DL). According to our investigation, up to 46.59% of the total 161 public reports confidently select SSDD to study DL-based SAR ship detection. Undoubtedly, this situation reveals the popularity and great influence of SSDD in the SAR remote sensing community. Nevertheless, the coarse annotations and ambiguous standards of use of its initial version both hinder fair methodological comparisons and effective academic exchanges. Additionally, its single-function horizontal-vertical rectangle bounding box (BBox) labels can no longer satisfy the current research needs of the rotatable bounding box (RBox) task and the pixel-level polygon segmentation task. Therefore, to address the above two dilemmas, in this review, advocated by the publisher of SSDD, we will make an official release of SSDD based on its initial version. SSDD’s official release version will cover three types: (1) a bounding box SSDD (BBox-SSDD), (2) a rotatable bounding box SSDD (RBox-SSDD), and (3) a polygon segmentation SSDD (PSeg-SSDD). We relabel ships in SSDD more carefully and finely, and then explicitly formulate some strict using standards, e.g., (1) the training-test division determination, (2) the inshore-offshore protocol, (3) the ship-size reasonable definition, (4) the determination of the densely distributed small ship samples, and (5) the determination of the densely parallel berthing at ports ship samples. These using standards are all formulated objectively based on the using differences of existing 75 (161 × 46.59%) public reports. They will be beneficial for fair method comparison and effective academic exchanges in the future. Most notably, we conduct a comprehensive data analysis on BBox-SSDD, RBox-SSDD, and PSeg-SSDD. Our analysis results can provide some valuable suggestions for possible future scholars to further elaborately design DL-based SAR ship detectors with higher accuracy and stronger robustness when using SSDD.

To realize autonomous ships in the near future, possibility of automatic berthing has been investigated. Automatic berthing is not an easy task because of some complexities that are included in the problem, such as the nonlinearity of the low-speed maneuvering model, danger of collision with berth, etc. In this research, as a first step, the authors solved the off-line automatic berthing problem. Here, the optimal control problem was modeled as minimum-time problem, and the collision risk with the berth was taken into account. The authors attempted to apply the covariance matrix adaption evolution strategy (CMA-ES), which is considered state-of-the-art in evolutionary computation approaches for optimization of real-valued variables. In the problem dealt with here, a propeller and a rudder were used only as control inputs; so, the degree of difficulty was significantly high. Nevertheless, optimal control method based on the CMA-ES successfully gave us the offline results for typical situations considered. It is noteworthy that preparation of a feasible initial control input was not required in the calculation process, which made the proposed procedure robust. The calculation method proposed here is offline, but the results could be applied as an initial guess in an online (real-time) control problem.

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.

Accurate ship berthing behavior prediction (BBP) is essential for enabling collision warnings and support decision-making. Existing methods based on Automatic Identification System (AIS) data perform well in the task of ship trajectory prediction over long time-series and large scales, but struggle with addressing the fine-grained and highly dynamic changes in berthing scenarios. Therefore, the accuracy of BBP remains a crucial challenge. In this paper, a novel BBP method based on Light Detection and Ranging (LiDAR) data is proposed. To test its feasibility, a comprehensive dataset is established by conducting on-site collection of berthing data at Dalian Port (China) using a shore-based LiDAR system. This dataset comprises equal-interval data from 77 berthing activities involving three large ships. In order to find a straightforward architecture to provide good performance on our dataset, a cascading network model combining convolutional neural network (CNN), a bi-directional gated recurrent unit (BiGRU) and bi-directional long short-term memory (BiLSTM) are developed to serve as the baseline. Experimental results demonstrate that the baseline outperformed other commonly used prediction models and their combinations in terms of prediction accuracy. In summary, our research findings help overcome the limitations of AIS data in berthing scenarios and provide a foundation for predicting complete berthing status, therefore offering practical insights for safer, more efficient, and automated management in smart port systems.

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.