Search Results (9)

Digitalization has revolutionized the maritime industry, particularly in navigation systems. The use of advanced tools such as the Electronic Chart Display and Information System (ECDIS) has increased the need for information processing. However, the complexity of these systems can be overwhelming for navigators. To address the concern of usability of these complex navigation systems, training with simulator data allows the crew to familiarize themselves with these systems, handle complex navigation scenarios effectively, support the transition from paper-based systems to digital systems, and help in improving their situational awareness (SA) at sea. We propose a tool that provides optimal conditions for assessing situational awareness and informing the development of intuitive systems and user interfaces. In the maritime safety domain, there is an inverse correlation between situational awareness and scenario/system complexity, highlighting the importance of effective training and assessments to improve SA. The proposed tool utilizes the Situational Awareness Global Assessment Technique (SAGAT) method, widely used in other domains, to calculate an individual’s SA score. It evaluates participants’ situational awareness in different navigational scenarios on Ship Handling Simulators, using dynamic questionnaires and contextual maps. Additionally, it integrates a rule-based system to assess participants’ performance and calculate a situational awareness score in real time, offering possibilities for assessing the SA of navigators. Full article

Nowadays, engine room simulators have become an important tool for maritime training, but programming engine room simulators often involves handling large amounts of data, making the process inefficient. This paper proposes an innovative visual modeling method for the ship pipeline network program in engine room simulators, aimed at addressing the heavy programming tasks associated with traditional text-based design and calculation methods when dealing with complex and large-scale pipeline systems. By creating Scalable Vector Graphics (SVG) images and using Windows Presentation Foundation (WPF) to place controls, an intuitive graphical user interface is built, allowing programmers to easily operate through the graphical interface. Subsequently, You Only Look Once version 5 (YOLOv5) object detection technology is used to identify the completed SVG images and WPF controls, generating corresponding Comma-Separated Values (CSV) files, which are then used as data input via C# (C Sharp). Through automated data processing and equipment recognition, compared to traditional manual design processes (such as using Matlab or C++ for pipeline design), this method reduces human errors and improves programming accuracy. Customization of key pipeline characteristics (such as maximum flow and flow direction) enhances the accuracy and applicability of the pipeline network model. The intuitive user interface design also allows nonprofessional users to easily design and optimize pipeline systems. The results show that this tool not only improves the efficiency of data processing and calculation but also demonstrates excellent performance and broad application prospects in the design and optimization of ship pipeline systems. In the future, this tool is expected to be more widely promoted in ship pipeline network education and practical applications, driving the field towards more efficient and intelligent development. Full article

Considerable technological progress has been made in ship handling and mooring in recent years, especially progress generated by the needs imposed by the introduction of ever larger ships. These advancements exploit the economic scale and environmental efficiency of larger vessels, but also present unique challenges, particularly in narrow waterways and harbour approaches. Precise navigation in these environments requires highly accurate hydrographic measurements, high-quality electronic charts, and advanced navigation systems, such as modern electronic chart display and information systems (ECDIS). Safe and efficient port operations also depend on the optimised allocation of port resources and comprehensive queuing strategies. Modern ships are increasingly susceptible to interference with Global Navigation Satellite Systems (GNSS) and Automatic Identification Systems (AIS), necessitating the development of resilient technologies and procedures to ensure navigational safety. In addition, climate change is exacerbating the challenges of ship handling in ports, as larger vessels are particularly vulnerable to sudden gusts of wind and have difficulty maintaining their position in the quay in strong crosswinds. Training and simulation are crucial to overcoming these challenges. Ship-handling simulators are invaluable for training purposes, but development is still needed to accurately simulate tilt and lean effects, especially when ships are sailing in narrow channels with following currents and changing winds. Improving the accuracy of these simulators will improve the preparation of seafarers for real-life conditions and ultimately contribute to safer and more efficient ship operations. Full article

The autonomous operation of a device or a system is one of the many vital tasks that need to be achieved in many areas of industry. This is also true for maritime transport. This paper introduces an approach developed in order to achieve the autonomous operation of a ship in a port. A safe trajectory was calculated with the use of the Ant Colony Optimization (ACO) algorithm. The ship motion control was based on two controllers: the master overriding trajectory controller (OTC) and the slave low speed controller based on the Linear Matrix Inequalities (LMI) method. The control object was the model of a Very Large Crude Carrier Blue Lady. The results of our simulation tests, which show the safe trajectories calculated by the ACO algorithm and executed by the ship using the designed controllers (OTC and LMI), are presented in this paper. The results present maneuvers executed by the Blue Lady ship when at port. The area where the tests were conducted is located in the Ship Handling, Research and Training Center of the Foundation for Shipping Safety and Environmental Protection on the Lake Silm in Kamionka, Poland. Full article

Ship maneuvering in ports is increasingly reduced because the increase in ship size is not proportional to the increase in port areas. Furthermore, the number of assisted vessels and the need for tugboats working in densely populated areas make it necessary to reduce the number of involved tugboats, reducing pollution and costs. Therefore, shiphandlers must know, in addition to the pivot point, the center of lateral resistance under any circumstance to optimize the assistance from tugboats and improve maritime navigation. From the literature, it is evident that the practical determination of the center of lateral resistance is still unknown. This paper aims to propose novel mathematical models to identify the position of this point and the most important variables that determine its position. For that, data of different ships in different conditions were obtained from a full mission bridge simulator. Afterwards, 15 novel mathematical models were developed, making use of artificial intelligence tools and training neural networks. The high determination factor reached in some models shows the accuracy of the obtained models. One advantage of the presented models is that they are very easy to be applied by shiphandlers, because highly well-known parameters are involved. Moreover, original 3D charts showing the combination of the input variables were generated to identify the map of the whole process. The very simple new models obtained and the novel 3D charts shown in the present paper can be considered useful and applicable by the shiphandlers of most of the merchant fleet to improve the efficiency and safety of maritime navigation in increasingly restricted waters. Full article

Through technological development, ships are being automated, reducing the number of human operators. Accordingly, the responsibility of humans becomes more significant, making a single operator’s proficiency count. Simulated ship-handling training evaluates trainees’ proficiency using specific criteria to verify proficiency. However, the present criteria are confined to training scenarios, and it is hard to determine whether trainees finally achieved expert-like ability. This research conducted probabilistic estimation on experts’ average steering angles. The paper contains the corresponding explanations for each step of the research methods, from the data preprocessing step to the probabilistic steering angle estimation. The research findings include the experts’ trendline of average steering angles and the sample trainee’s evaluation results. Full article

Ship collisions often result in huge losses of life, cargo and ships, as well as serious pollution of the water environment. Meanwhile, it is estimated that between 75% and 86% of maritime accidents are related to human factors. Thus, it is necessary to enhance the intelligence of ships to partially or fully replace the traditional piloting mode and eventually achieve autonomous collision avoidance to reduce the influence of human factors. In this paper, we propose a multi-ship automatic collision avoidance method based on a double deep Q network (DDQN) with prioritized experience replay. Firstly, we vectorize the predicted hazardous areas as the observation states of the agent so that similar ship encounter scenarios can be clustered and the input dimension of the neural network can be fixed. The reward function is designed based on the International Regulations for Preventing Collision at Sea (COLREGs) and human experience. Different from the architecture of previous collision avoidance methods based on deep reinforcement learning (DRL), in this paper, the interaction between the agent and the environment occurs only in the collision avoidance decision-making phase, which greatly reduces the number of state transitions in the Markov decision process (MDP). The prioritized experience replay method is also used to make the model converge more quickly. Finally, 19 single-vessel collision avoidance scenarios were constructed based on the encounter situations classified by the COLREGs, which were arranged and combined as the training set for the agent. The effectiveness of the proposed method in close-quarters situation was verified using the Imazu problem. The simulation results show that the method can achieve multi-ship collision avoidance in crowded waters, and the decisions generated by this method conform to the COLREGs and are close to the level of human ship handling. Full article

A very significant number of marine accidents occur because of human errors. This study aimed to prevent ship collisions by identifying types of navigators’ errors. Based on Reason’s classification theory, the possible human errors are classified into skill-based slips (SBSs) (errors caused by the lack of skills), rule-based mistakes (RBMs) (errors caused by the misapplication of rules), and knowledge-based mistakes (KBMs) (errors caused by the lack of navigator’s knowledge). For this study, a scenario-based experiment using a ship-handling simulator was conducted with 50 recruited student navigators. The results revealed two primary human errors of accidents, namely lack of knowledge and misapplication of rules. The results suggest that a collision can be minimized when a navigator has sufficient knowledge of an appropriate course of action and a deep understanding of safety rules. Accidents cannot be prevented by identifying errors, but steps can be taken to narrow the knowledge gap. Based on the results, we proposed a simulation training on navigator error in an unfamiliar situation. The results are expected to reduce errors in the maritime sector using a human-centric work system. Full article

This article deals with the topic of modelling the log-yard of one of our industry partners. To this end, our framework is based on discrete-events modelling (DEM), as consequence that many stages of the process run as a sequence of events. The sequence starts when trucks, trains or ships arrive loaded with logs to the log-yard. A machine unloads these logs and accumulates them in different storage areas. Consequently, a machine transports logs from these areas to the pulp mill, thus finishing the process. As using probability density functions is the core concept of DEM, the necessary process data to build these PDFs have been partly provided by the company. Other necessary data have been acquired through time studies, and by defining operational requirements. The company data tell when trucks, trains, or ships arrive to the log-yard, and the amount of volume they carry. The objective is to develop the necessary formulations, model calibration techniques, and software, such that computer simulations reproduce the quantities observed in these data. To this end, this work suggests two alternatives to analyse the data itself. These two alternatives lead to two different models: (1) The first being a hybrid model, in the sense that it involves the events in the process, and the logic decisions taken by machine operators for handling the incoming load, and (2) the second containing only the main mathematical essence of the process. After running 100 simulations, both mathematical models show that the simulated values for input and output, in terms of transport units and their volume, differ only by less than 3% compared to company data. The first model has also shown the ability to replicate the decision making that a machine operator undergoes for driving the logs to the storage areas, and from there to the mill. Therefore, the framework adopted provides the necessary mathematical tools and data analysis to model the log-yard and obtain highly reliable results via simulations. Full article