In 1915 while the Imperial Trans-Antarctic Expedition's vessel Endurance was icebound in the Weddell Sea, lunar occultation timings were carried out in order to rate the chronometers and thereby find longitude. The original observations have been re-analysed using modern lunar ephemerides and catalogues of star positions. The times derived in this way are found to differ by an average of 20 s from those obtained during the expedition using positions given from the Nautical Almanac and introduces an additional offset of the true positions to the east of those recorded in the log.

Despite the crucial role played by nautical cartography during the 16th-century Iberian Expansion, surviving examples of charts used at sea are extremely rare, leaving gaps in our knowledge of how they were used in practice during this pivotal period. The present paper sheds light on this matter by introducing one of earliest extant Portuguese charts presenting positional fixes, which have so far gone unnoticed by researchers: the anonymous chart of about 1524 kept at the James Ford Bell Library. It is demonstrated that a second chart, kept at the Harry Ransom Center, was made by the same author from the same pattern. The production context of the charts is evidenced through a description of the manuscript copying technique most likely employed, and the rules governing chart manufacture in 16th-century Portugal. Exceptional aspects of the charts’ placenames are discussed, and a possible source for their geographical design is identified.

Almost all robust stereo visual odometry work uses the random sample consensus (RANSAC) algorithm for model estimation with the existence of noise and outliers. To date, there have been few comparative studies to evaluate the performance of RANSAC algorithms based on different hypothesis generators. In this work, we analyse and compare three popular and efficient RANSAC schemes. They mainly differ in using the two-dimensional (2-D) data points measured directly and the three-dimensional (3-D) data points inferred through triangulation. This comparison presents several quantitative experiments intended for comparing the accuracy, robustness and efficiency of each scheme under varying levels of noise and different percentages of outlier conditions. The results suggest that in the presence of noise and outliers, the perspective-three-point RANSAC provides more accurate and robust pose estimates. However, in the absence of noise, the iterative closest point RANSAC obtains better results regardless of the percentage of outliers. Efficiency, in terms of the number of RANSAC iterations, is found in that the relative speed of the perspective-three-point RANSAC becomes superior under low noise levels and low percentages of outlier conditions. Otherwise, the iterative closest-point RANSAC may be computationally more efficient.

A weather routing tool is presented based on forecasted weather data along the route and considering safety aspects. The tool aims to determine the optimal path for the minimisation of the fuel oil consumption, ensuring a safe passage. It is developed in MATLAB and considers detailed ship characteristics. Specifically, ship's motions and fuel oil consumption of the main engine during a potential path are estimated. For the latter, a physics-based model for a specific vessel is developed where tools of different level of detail are utilised to calculate the various resistance components. A speed management strategy along the route is specified as well as safety criteria representing acceptable limits of ship's responses. When the set criteria and constraints have been set, a genetic algorithm is used to find the optimal route by means of ship's heading or by considering both heading and ship's power settings as variables to minimise the fuel oil consumption. The search space of the algorithm lies within a predefined envelop, but still the evolutionary optimisation approach used has no pre-assigned values to any possible candidate waypoint.

The understanding and interpretation of simplicty in the context of a navigational path can be ambiguous. Different approaches to path planning are briefly juxtaposed, focusing on their simplicity-based distinctive features. This takes into consideration the response to drift caused by air or water currents and the geometric background. Special attention is paid to the straight-line-based solutions which are preferred by the human convenience and perception, recalling some well-known applications in navigation and including their (dis)advantages. In particular, this refers to the Euclidean simplicity applied at the cost of effectiveness in the standard search and rescue (SAR) patterns in the presence of relatively stronger position-dependent currents.

The three research topics, ship collision risk assessment, ship traffic hotspot detection and prediction, and collision-avoidance based ship path planning, are vital for next-generation vessel traffic management and monitoring systems. The system development is closely related to big data analytics and artificial intelligence for restricted waters. This study, therefore, aims to analyse the state-of-the art of these three topics over the latest decade, identify research gaps, and shed light on future research avenues. To achieve these three objectives, we critically and systematically review related articles that were published during the period between 2011 and 2021. We believe that this comprehensive and critical literature review would have a significant and profound impact on the formal safety assessment and vessel traffic management, and monitoring studies because it is not only an extension but also an essential continuity work of the literature review on maritime waterway risk assessment and prediction, as well as ship path guidance for ship collision risk mitigation in accordance with current automation vessels development and modern intelligent port construction.

There is an undeniable recognition that maritime cybersecurity risk management should involve process, technology, and people. However, thus far, most studies have focused on the technical and process aspects of maritime cybersecurity, more than on the human element. On a vessel, the Electronic Chart Display and Information System (ECDIS) is, amongst all the electronic devices on the bridge, a complex and indispensable maritime sociotechnical system that must consider both technical and human aspects. In the context of maritime cyber resilience, it is important to note that when developing strategies for maritime cybersecurity, one cannot only consider technical security measures and ignore human error, as this does not adhere to good cybersecurity practice. To address this, this study aims to identify the navigating officers’ responsibilities for ECDIS cybersecurity and find the human error probabilities during these tasks via the SLIM-based human reliability analysis method. The outputs of this study provide an insight for industrial policies and best practices, in ECDIS cybersecurity risk management in terms of the behavioural and cultural aspects of shipping.

Inland waterway transportation is one of the most important means to transport cargo in rivers and canals. To facilitate autonomous navigation for ships in inland waterways, this paper proposes a data-driven approach for predictions and control of underactuated ships with unknown dynamics, which integrates model predictive control (MPC) with an iterative learning control (ILC) scheme. In each iteration, kernel-based linear regressors are used to identify the relations between the evolution of ship states and control inputs based on the stored data from previous iterations and the collected data during operation, so as to build the system prediction model. The data are dynamically used to fix the prediction model over iterations, as well as to improve the controller performance until it converges. The proposed approach does not require prior knowledge regarding the hydrodynamic coefficients and ship parameters, but learns from the data instead. In addition, it exploits the advantages of MPC in handling constraints with minimised overall cost. Simulation results show that the controller could start from a nominal, linear data-driven ship model and then learn to reduce the path-following errors based on the data obtained over iterations.