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Assisting harbour tugboats are essential for safe navigation in ports and for berthing/unberthing operations. Because the port infrastructure needs to be updated in the coming age of autonomous ships, autonomous tugboats or remotely controlled tugboats are expected to be part of the navigation assistance provided by future ports. Although there have been some studies on the design of cooperative control of tugboats, few studies have focused on the mathematical model of their cooperative manoeuvres. Particularly, in the case of pushing assistance, tugboats have often been treated simply as a kind of side thrusters attached to the assisted ship. Thus, we present a new framework of the mathematical model for cooperative manoeuvres that considers the coupled motions among tugboats and a ship as precisely as possible. Solving the dynamics of all tugboats as well as the assisted ship can render the model more advanced and realistic, and is the most significant contribution of this study. The simulation tool based on the proposed model can be used as a plant model in designing and verifying the tugboat’s manoeuvring control system in the future. In this study, as examples, considering a tentative control method, some unique scenarios were simulated to demonstrate the cooperative manoeuvres.

The global greenhouse gas emitted from shipping activities is one of the factors contributing to global warming; thus, there is an urgent need to mitigate the adverse effect of climate change. One of the key strategies is to build a vibrant maritime industry with the use of innovation and digital technologies as well as intelligent systems. The digitization of the shipping industry not only provides a competitive edge to the shipping business model but also enhances ship operational and energy efficiency. This review paper focuses on the big data analytics and machine learning applied to harbour craft vessels with the aim to achieve fuel efficiency. The paper reviews the telemetry system requires for the digitalization of harbour craft vessels, its challenges in installation, the vessel monitoring and data transmission system. The commonly used methods for data cleaning are also presented. Last but not least, the paper considers two types of the machine learning systems, i.e., supervised and unsupervised machine learning systems. The multi-linear regression and hidden Markov model for supervised machine learning system and the artificial neural network, grey box model and long short-term memory model for unsupervised machine learning are discussed, and their pros and cons are presented.

Selection of a tugboat to be used in a port according to the operations to be carried out is a difficult problem that requires many criteria to be evaluated at the same time. This selection requires high experience as well as technical knowledge about tugboats and operations. In this study, a numerical analysis was carried out within the framework of design, operational and financial criteria to develop a method to select a tugboat. The propulsion/manoeuvring systems used in the tugboats were investigated and different criteria related to the tugboats with different propulsion systems were evaluated by subject matter experts through a survey including technical and financial data. The responses were interpreted with the fuzzy analytic hierarchy process to select a suitable tugboat alternative based on the type of propulsion system.

For safe ship berthing, all steps involved, from bringing the ship to the fairway area to directing it to the final wharf position at a desirable speed, need to be performed properly. In this article, a support system is proposed to automatically bring the ship into the berth after completing the maneuvering process. This proposed system requires no involvement of a shipmaster, enabling automatic ship berthing in marine practice. Three fuzzy controllers are proposed to conduct different tasks of berthing. The first controller is designed to control the movement of the ship in a longitudinal direction towards the wharf while the second controller stabilizes the relative bearing error with respect to the tugboat. The final controller is responsible for bringing the ship into a wharf according to crabbing motion with bow thruster and tugboat simultaneously. Compared to other approaches, ours has a wider range of adoption, better generalization capability, and is more suitable for marine practice. Comprehensive experiments in numerical simulations are carried out, showing the superior performance of the proposed support system for automatic ship berthing.

The recent researches on the automatic berthing control problems have used various kinds of tools as a control method such as expert system, fuzzy logic controllers and artificial neural network (ANN). Among them, ANN has proved to be one of the most effective and attractive options. In a marine context, the berthing maneuver is a complicated procedure in which both human experience and intensive control operations are involved. Nowadays, in most cases of berthing operation, auxiliary devices are used to make the schedule safer and faster but none of above researches has taken into account. In this study, ANN is applied to design the controllers for automatic ship berthing using assistant devices such as bow thruster and tug. Using back-propagation algorithm, we trained ANN with set of teaching data to get a minimal error between output values and desired values of four control outputs including rudder, propeller revolution, bow thruster and tug. Then, computer simulations of automatic berthing were carried out to verify the effecttiveness of the system. The results of the simulations showed good performance for the proposed berthing control system.