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Marine cranes are one of the most important industrial equipment in the maritime field. The base of a marine crane is dynamically moving as the motion of the ship's six degrees of freedom that is affected by offshore environmental loads. There is a coupling between the crane and the ship, which means the crane operation and the ship motion affect each other. In this paper, co-simulation technology is employed to construct the virtual marine operation system which is composed of diverse Functional Mock-Up Units (FMUs) exported using the Functional Mock-Up Interface (FMI) standard and System Structure and Parameterization (SSP) standard to define the structure and parameters based on the co-simulation platform Vico. A path planning case for the Palfinger crane is implemented using the A* algorithm. The physical three-dimensional working space of the crane is discretized into a finite number of nodes in joint space. The cost is defined by the variable of the ship motion to optimize the marine operation. The obtained discrete nodes are smoothed to get the velocity of the actuators as control signals. Simulation of the crane operation is carried out in the virtual operating system following the planned path. [ABSTRACT FROM AUTHOR]

The maritime industry could face major changes the coming decade. Technology development opens for new ways of operating vessels and autonomy is argued to revolutionise design and operations. However, despite a large focus on autonomy for several years, no autonomous concepts have become operational. In our paper, we suggest an initial step towards autonomy using unmanned automated vessels. To explore this, we utilize the coherence between the systems thinking and participatory design thinking process. We frame the project to focus on the Vessel Traffic Services (VTS) traffic regulation and organisation measures in a 2025 scenario. The study shows that the standardisation of traffic would be beneficial for the present MTS with only conventional vessels, it will be a prerequisite for a future MTS with automated vessels. Further, we identify that the VTS need to change their role from solving situations ad-hoc to assume a tactical responsibility in traffic planning and to resolve situations at an earlier stage. A prototype of the future MTS shows that the identified challenges are considered possible to solve within a short time frame and is summed up in one statement from a plenary discussion: 'this is not difficult, we could do it tomorrow'. [ABSTRACT FROM AUTHOR]

A proper interpretation and classification of navigators’ operational behaviors is crucial to the design of onboard decision-support systems. This research work dives into the study of navigators’ navigating patterns (NPs) in a maritime collision-avoidance (CA) traffic situation. Three NPs, specifically conservative, moderate, and aggressive modes, are identified with respect to a collision risk assessment (CRA) by interpreting data collected from the GPS and automatic identification systems. The CRA is realized following the collision risk modeling concept of the closest point of approach. Then, a human-centered onboard guidance-support system is developed according to the patterns identified to help navigators make decisions. This proposed approach is implemented in the scenario of sailing across a narrow strait, where human intelligence remains necessary in the foreseeable future. The research experiment was conducted on Kongsberg maritime simulators. Thirty-six rounds of sailing data containing 108 CA subtasks were collected and analyzed to classify NPs. Afterward, a guidance-support system was designed based on the patterns’ demonstration. An additional experiment to test the developed system in the same scenario was organized on the same simulator. The results show that the system can considerably improve the navigator’s navigation management ability in CA operations. Our approach combines data analysis and risk modeling with authentic human-operated navigating data and traffic information, which makes it distinct from traditional intuitive and cognitive maritime traffic modeling. It is the first one that defines NPs and puts them into potential industrial application pragmatically. [ABSTRACT FROM AUTHOR]

One objective of smart cities is to produce smart decisions that will address sustainability and climate change through big data and citizen engagement. However, to achieve this, local governments must address two barriers to citizen engagement: concerns about data privacy and the difficulty big data poses to nonexperts. Big data by its nature produces a wide range of varying insights; however, it is difficult for nonexperts to understand the situations these insights describe, much less determine, and communicate priorities and solutions. [ABSTRACT FROM AUTHOR]

This paper reports a multiple case study of a training center collaboration with three offshore companies and a coastline authority. Through a qualitative inquiry, we utilized the actor-network theory to analyze the common understanding of simulator use in these organizations. The paper argues that the simulator itself is an actor that can integrate shared interests with other actors to establish an actor-network. Such an actor-network expands simulator use beyond purely training purposes. It advocates that the simulator is a medium between maritime academia and industry and aligns it with the same actor-network to facilitate the process of "meaning construction." Such a meaning construction process offers simulator-based training with a valuable definition of the learning outcomes. It helps clarifying who will gain the benefits from simulator use in the future, as well as when and on what basis. The paper also reflects on the benefits and limitations of utilizing a multiple case study in the maritime domain. [ABSTRACT FROM AUTHOR]

This paper suggests a framework about how log data are used to develop a classifier to recognise the sailing status of a commuter ferry, which, in turn, serves as a tool of safety awareness. Several sailing scenarios are defined under the expertise's interpretation based on log data. A classifier is developed by support vector machine algorithm to recognise different scenarios. The classifying precision is getting improved as the database getting larger. Heat maps are drawn statistically to obtain the likelihood site of each sailing status. Contour maps are drawn by interpolation according to heat maps. Based on contour maps, two evaluation items are proposed to reflect the safety level. The safety level term is used for optimising the control. The established classifier has a recognition precision over 96 percent. A path following simulation is executed to verify the effectiveness of the safety level for enhancing sailing safety. [ABSTRACT FROM AUTHOR]

Ship intelligence has been a hot topic in recent years. How to achieve autonomous maneuvers in a complex marine environment in a safe, efficient, and low-cost manner is a fundamental task that ocean engineers face. This paper presents a two-stage trajectory planning scheme to address the minimum-time maneuvering problem in close-range encounters. The scheme is robust and versatile, as it can deal with the complex spatial variability, such as sea current, state constraints, marine traffic, and physical constraints, of close-range maneuvering. In the first stage, a directed graph with variable length is generated according to the sea current distribution. A wavefront search is applied on the graph to explore the reachability, the cost of state constraints, and the risk of collision. After a discrete solution has been found, the second stage involves searching for a smooth solution. A Bézier curve based parameter optimization approach is proposed to get rid of limited moving directions in the directed graph and explore around the discrete path. The result will be a near-optimal, smooth path. The proposed scheme has been tested to solve the Zermelo's ship steering problem and several other close-range maneuvering problems. The results demonstrate that the scheme is efficient in generating smoothed minimum-time trajectories for surface vessels when maneuvering in close-range encounters. [ABSTRACT FROM AUTHOR]

The sea-state estimation is a fundamental problem in the development of autonomous ships. Traditional methods such as wave buoy, satellites, and wave radars are limited by locations, clouds, and costs, respectively. Model-based methods are prone to incorrect estimations due to their high dependence on mathematical models of ships. As previous data-driven studies for sea-state estimation consider only wave height and use the motion data from dynamic positioning (DP) vessels, this article introduces a new, deep neural network (SSENET) to estimate sea state in light of both wave height and wave direction and extends the generality of sensor data from ship motion with forward speed. SSENET is built on the basis of stacked convolutional neural network (CNN) blocks with dense connections between different blocks, channel attention modules, and a feature attention module. The dense connections build short-cut paths between input and all subsequent convolutional blocks, which can make full use of all the hierarchical features from the original time-series sensor data. The channel attention modules aim to enhance the features extracted by each convolution block. The feature attention module focuses on combining the feature fusion of hierarchical features in an adaptive manner. Benchmark experiments show the competitive performance against the state-of-the-art approaches. Applying the SSENET on two data sets of a zigzag motion for comparative studies shows the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The Vessel Traffic Services is a control system in the maritime traffic system where the VTS operators need to cope with a complex environment to contribute to safe and efficient ship movements. By using cognitive task analysis (CTA), the study has explored how the VTS operators cope with complexity. The study shows how complexity requires the operators to have a large variety in their responses, and these responses are based on personal experience, organisational knowledge, teamwork, and communication. However, the combination of the operators' experience and complexity in the environment lead to a large variation in the operators' response. [ABSTRACT FROM AUTHOR]

Researchers have been investigating data-driven modeling as a key way to achieve ship intelligence for years. This paper presents a novel data analysis approach to data-driven modeling of ship motion. We propose a global sensitivity analysis (GSA) approach combining artificial neural network (ANN) and sparse polynomial chaos expansion (SPCE) techniques to accommodate high-dimensional sensor data collected from ship motion. An ANN is constructed as a surrogate model to associate ship sensor data with a certain type of ship motion. To account for the computational efficiency of GSA, an SPCE is integrated into the GSA to decrease the need for Monte Carlo (MC) samples generated by the ANN. A probe variable is designed to couple with the MC samples, which plays a role in determining the degree of convergence of variable importance. A test on benchmark function demonstrates the efficiency and accuracy of the proposed approach. A case study of ship heading with and without environment effects is conducted. The experimental results show that the proposed approach can identify and rank the most sensitive factors of ship motion. The proposed approach highlights the application of GSA in data-driven modeling for ship intelligence. [ABSTRACT FROM AUTHOR]

Improving the training programs for maritime operations is beneficial to enhance the maritime safety in practice. In this article, we propose a novel approach to the assessment of visual attention in a maritime operation so as to support an expert-in-the-loop training program. Experts’ knowledge of maritime operation and experiences in the simulator are incorporated into the training program in three ways. First, through a questionnaire, information about task division, identification of critical operation, and definitions of areas of interest (AOIs) are incorporated as prior knowledge for modeling visual attention. Second, a weight scale factor that emphasizes the high importance of visual focus in critical operations is utilized to generate an operations-dependent attention map. Third, based on an expert's attention map and visual switch between AOIs, a similarity metric is designed as a comprehensive evaluation between saliency and visual transition. A case study of heavy lifting operation is performed by two groups of trainees who have received different briefings about “critical operation.” The assessment result shows that the group with more detailed briefing obtains a 6% similarity score higher than the other group, which is consistent with the debriefing result of a superior performance of that group. The proposed approach is thus verified effective in assessing visual attention for the expert-in-the-loop training program. [ABSTRACT FROM AUTHOR]

Features of fish-like environmental compatibility and maneuverability have attracted bioinspired design researchers worldwide to contemplate the practical applications of robotic fish. This article presents a conceptual design for and the development of a robotic fish inspired by the cownose ray. We extracted essential biomimetic parameters of the cownose ray to develop reasonable simplifications of the body shape, the mechanical structure design principle of the multijointdriving fin rays, and the motion principle. The practical motion abilities of the bionic prototype's internally driven skeleton are calculated theoretically and compared with its natural model. Parameters affecting propulsion performances are analyzed using a one-dimensional (1-D) calculation method. The basic motion modes are obtained according to the analysis. [ABSTRACT FROM AUTHOR]

This paper presents a new analysis approach for evaluating situation awareness in marine operation training. Taking advantage of eye tracking technology, the situation awareness reflected by visual attention can be visualized and analyzed. A scanpath similarity comparison method that allows group-wise comparisons is proposed. The term 'Expert zone' is introduced to evaluate the performance of novice operator based on expert operators' eye movement. It is used to evaluate performance of novice operators in groups in certain segment of marine operation. A pilot study of crane lifting experiment was carried out. Two target stages of operation for the load descending until total immersion to the seabed were selected and analyzed for both novice and expert operators. The group-wise evaluation method is proven to be able to access the performance of the operator. Besides that, from data analysis of fixation-related source and scanpath, the similarities and dissimilarities of eye behavior between novice and expert is concluded with the scanpath mode in target segment. [ABSTRACT FROM AUTHOR]

Bionic pectoral fins play an essential role in achieving the superior swimming ability of the bio-inspired undersea monitoring platform by producing desired movements and propulsion forces. This paper discusses the hydrodynamic modelling and experiments of a bionic pectoral fin inspired by the cownose ray. A simplified hydrodynamic model of the oscillatory bionic pectoral fin is built. A physical prototype of the bionic pectoral fin is developed based on the structure and movement parameters extracted from the cownose ray. Performances of the bionic pectoral fin in producing lift force and propulsion force are tested using a towing-tank experimental platform and compared with the results from corresponding simulations. Feasibility of the hydrodynamic simulation and the bionic design method are verified. [ABSTRACT FROM AUTHOR]