To eliminate the effect of ocean currents for optimal path planning for unmanned underwater vehicles (UUVs) in the underwater environment, an intelligent algorithm is designed and proposed in this paper. The algorithm consists of two parts: an artificial potential field-based algorithm that derives the shortest path and avoids collision accidents; and an adjusting function that eliminates the effect of ocean currents. The planning results of the intelligent algorithm are presented in detail, and compared with the conventional algorithm that does not consider the effect of currents. The effectiveness of the optimised path planning method given in this paper is proved.

It is a well-known fact that humans are subject to circadian alterations caused by the action of sunlight. This strongly affects their behaviour and skill, making them change their level of alertness from high at daytime to low at night-time. Taking into account this biological feature, and considering that commercial shipping is one of the oldest round-the-clock activities, raises the question whether bridge watchkeepers can carry out their tasks on night shifts with the same level of alertness as on day shifts. Furthermore, night bridge watchkeeping has to be performed in darkness or very dim light to improve the visual capacity of lookouts. A sample of vessel collisions was analysed and it was find out that this kind of accident tends to be more frequent and more serious during the darkness period of the solar day.

Using near miss data detected from non-accident information to analyse marine traffic risk can alleviate some of the limitations of accident-based methods. A model based on an arena for detecting scenes of near miss is proposed to detect comprehensively those ship encounters with potential collision risk. To eliminate the influence of data sampling frequency on the detection of scenes of near miss, a single near miss is defined as the whole progress of traffic state from the time the target ship sails into the arena of the subject ship to the time it leaves the arena of the subject ship. To research the geographical distribution characteristics of marine traffic risk, first, a statistical model for the scenes of near miss based on the water grid method is proposed, and then a macroscopic collision risk model based on near miss is developed. The geographical distribution characteristics of marine traffic risk in the Bohai Sea are analysed, and the water areas of high marine traffic risk are obtained. The findings can provide theoretical support for marine safety management.

This paper proposes a novel method of error mitigation for stereo visual odometry (VO) applied in land vehicles. A non-holonomic constraint (NHC), which imposes physical constraint to the rightward velocity of a land vehicle, is implemented as an observation in an extended Kalman filter (EKF) to reduce the drift of stereo VO. The EKF state vector includes position errors in an Earth-centred, Earth-fixed (ECEF) frame, velocity errors in the camera frame, angular rate errors and attitude errors. All the related equations are described and presented in detail. In this approach, no additional sensors are used but NHC, namely velocity constraint in the right direction , is applied as an external measurement to improve the accuracy. Tests are conducted with the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) datasets. Results show that the relative horizontal positioning error improved from 0⋅63% to 0⋅22% on average with the application of the velocity constraints. The maximum and root mean square of the horizontal error with velocity constraints are both reduced to less than half of the error with stand-alone stereo VO.

Reasonable stochastic model and function model are the premise of accurate velocity determination, especially in the time-differenced carrier-phase (TDCP) method. This paper presents, first, an elevation-dependent stochastic model (ESM), and then gives a simplified and unified Galileo/GPS combined TDCP function model, where the inter-system bias (ISB) variations are analysed based on correlation coefficients and the scaled sensitivity matrix. To evaluate the performance of the proposed models, datasets collected at 10 multi-GNSS experiment (MGEX) stations and a vehicle kinematic experiment are employed. The results indicate that the ESM model can improve the accuracy of the velocity solution, especially for the Galileo/GPS combined system, in comparison with the equivalent weight ratio method. In contrast to the Galileo-only velocity solution, the Galileo/GPS combined velocity solution can bring improvements of about 1–1⋅5 mm/s, 0⋅5 mm/s and 1⋅5–2⋅5 mm/s in East, North and Up components, respectively. Compared with the traditional Galileo/GPS TDCP model, the simplified and unified model shows no obvious differences in all components in the environment with more visible satellites, but it performs better in a challenging environment with few visible satellites.

Oceanus is a hardware and software platform designed and developed to provide useful information to the crew of a racing yacht. The key features of the proposed solution are its reliability, the possibility to extend and customise it, and its context-awareness, which simplifies its usage in an intelligent way. The target users of the system are both beginners who want a navigation aid, but cannot afford the expensive and often overly complicated commercial systems available on the market, and more experienced sailors who can benefit from an open and customisable instrument to study and fine-tune the setup and performance of their sailing boats. Furthermore, Oceanus strives to be as much as possible a low-cost architecture, both in software and hardware requirements.

This paper addresses the fusion of the pseudorange/pseudorange rate observations from the global navigation satellite system and the inertial–visual simultaneous localisation and mapping (SLAM) to achieve reliable navigation of unmanned aerial vehicles. This work extends the previous work on a simulation-based study [Kim et al. (2017). Compressed fusion of GNSS and inertial navigation with simultaneous localisation and mapping. IEEE Aerospace and Electronic Systems Magazine, 32(8), 22–36] to a real-flight dataset collected from a fixed-wing unmanned aerial vehicle platform. The dataset consists of measurements from visual landmarks, an inertial measurement unit, and pseudorange and pseudorange rates. We propose a novel all-source navigation filter, termed a compressed pseudo-SLAM, which can seamlessly integrate all available information in a computationally efficient way. In this framework, a local map is dynamically defined around the vehicle, updating the vehicle and local landmark states within the region. A global map includes the rest of the landmarks and is updated at a much lower rate by accumulating (or compressing) the local-to-global correlation information within the filter. It will show that the horizontal navigation error is effectively constrained with one satellite vehicle and one landmark observation. The computational cost will be analysed, demonstrating the efficiency of the method.

For more efficient aerial surveillance, charging pads are set up at corresponding distances so that an unmanned aerial vehicle (UAV) can sustain its operations without landing. Usually manual intervention is required to land a UAV for charging and so extend its mission. To enable a UAV to operate autonomously, wireless power charging using inductive coupling is proposed. Using this method, the UAV's battery is charged until it reaches the next charging station. This paper focuses on two significant aspects of the process: vision-based navigation for charging pad detection, and wireless power charging. The coils were designed, and other parameters like mutual inductance, coupling coefficient and the distance between the coils for effective power transmission were analysed, using Ansys and Maxwell software. A quadcopter was built, with battery and Lidar sensor connected to the Arduino controller for low battery voltage detection and height measurement, respectively. Whenever the battery voltage is low, the UAV is steered towards the nearest charging pad using the global position navigation system. To test the process, the quadcopter was flown over the charging pad using a vision-based algorithm pre-defined in the image processor (Raspberry Pi B+).

The very high frequency data exchange system (VDES) is promising in promoting electronic navigation (E-navigation) and improving navigation safety. The multiple access control (MAC) protocol is crucial to the transmission performance of VDES. The self-organising time division multiple access (SOTDMA) protocol, as the only access mode given by current recommendations, leads to a high rate of transmission collisions in the traditional automatic identification system (AIS), especially with heavy traffic loads. This paper proposes a novel feedback based time division multiple access (FBTDMA) protocol to address the problems caused by SOTDMA, such that collision of transmissions can be avoided in information transmission among vessels. Simulation results demonstrate that the proposed FBTDMA outperforms the traditional SOTDMA in terms of channel utilisation and throughput, and significantly reduces the transmission collision rate. The study is expected to provide insights into VDES standardisation and E-navigation modernisation.

This paper describes a camera simulation framework for validating machine vision algorithms under general airborne camera imperfections. Lens distortion, image delay, rolling shutter, motion blur, interlacing, vignetting, image noise, and light level are modelled. This is the first simulation that considers all temporal distortions jointly, along with static lens distortions in an online manner. Several innovations are proposed including a motion tracking system allowing the camera to follow the flight log with eligible derivatives. A reverse pipeline, relating each pixel in the output image to pixels in the ideal input image, is developed. It is shown that the inverse lens distortion model and the inverse temporal distortion models are decoupled in this way. A short-time pixel displacement model is proposed to solve for temporal distortions (i.e. delay, rolling shutter, motion blur, and interlacing). Evaluation is done by several means including regenerating an airborne dataset, regenerating the camera path on a calibration pattern, and evaluating the ability of the time displacement model to predict other frames. Qualitative evaluations are also made.

Spoofing is a kind of deliberate interference that aims to manipulate global navigation satellite system (GNSS) receivers into counterfeit position solutions. Conventional anti-spoofing methods are implemented prior to the calculation of the position solution, depending on the specific spoofing attack mechanisms. The paper presents a spoofing detection and mitigation method implemented in the position domain. The proposed method projects the correlograms of the visible satellites to a position-clock bias domain to construct the position domain projected correlogram. P(Y) code signatures retrieved from a reference station receiver are used to identify the counterfeit position solution and remove it from the victim receiver. Compared with the conventional single-channel spoofing detection technique, the proposed anti-spoofing method is more robust against thermal noise by combining the energy from multiple satellites. Detailed mathematical derivation of the statistical characteristics of this method is presented. Its effectiveness is validated using a realistic dataset generated by a Spirent GNSS simulator and NordNav wideband front-end. Results show that the proposed algorithm is capable of not only detecting a spoofing attack but also removing the spoofing effect from the victim receiver.

The probability of a human-caused incident occurring during dynamic positioning (DP) drilling operations is determined in this paper using binary logistic regression models built with data on 42 incidents that took place during the period 2011–2015. For each case, a range of variables characterising the configuration of the DP system, weather conditions and water depth are taken into account. These variables are taken into account to develop a logistic regression model that shows the likelihood of an incident being caused by human error. The results obtained show that human-based incidents are significantly more likely to occur when there is a lower usage of thrusters. These results are useful for focusing our attention on variables that may be associated with incidents attributable to human error, as well as for setting operational limits that could help to prevent these incidents and improve safety during these operations.

Various studies suggest that the maritime industry will continue to face the challenge of seafarer shortages. Young seafarer turnover has become a serious issue that cannot be underestimated. This paper aims to identify the root causes of young seafarer attrition in China and explore relevant solutions. It collects information via semi-structured interviews and questionnaires. Independent sample t-test, one-way ANOVA and least-significant difference are utilised for the variance analysis. The findings of the study show that occupational recognition and family responsibility are the two major factors contributing to young seafarers’ outflow. Chinese seafarers’ health status is another important factor that has received little attention. In addition, young seafarers of 31–35 years old have the most possibility of turnover, due to a number of reasons discussed in this paper. Age 40 or thereabouts is viewed as the watershed moment in a seafarer's career, so efforts should be made to help young seafarers pass through the hard period in their early thirties. This paper suggests that a clear career plan could be a potential solution to retain this backbone group as prospective senior officers.

Investigating the underlying predictors of speeding behaviour deserves the full attention of research. This study aims to examine the effects of demographic variables on the perceived deterrent mechanisms and to predict speeding behaviour to target appropriate prevention programmes. In this study, 212 randomly selected drivers having a valid car driving licence participated in an online survey. The results revealed that demographic variables influenced drivers’ perceptions towards social and legal sanctions as well as material loss. The model revealed that two sanction-related constructs, that is, legal sanction (b = −0⋅227, P = 0⋅007) and material loss (b = −0⋅218, P = 0⋅005), as well as lax perception towards traffic accident (b = −0⋅176, P = 0⋅025), were the significant predictors of speeding behaviour. These findings suggested that prevention programmes should prioritise young and single drivers. The most effective targeted prevention programmes are highlighted accordingly based on the study results.

In the integrated navigation system using extended Kalman filter (EKF), the state error conventionally uses linear approximation to tackle the commonly nonlinear problem. However, this error definition can diverge the filter in some adverse situations due to significant distortion of the linear approximation. By contrast, the nonlinear state error defined in the Lie group satisfies the autonomous equation, which thus has distinctively better convergence property. This work proposes a novel strapdown inertial navigation system (SINS) nonlinear state error defined in the Lie group and derives the SINS equations of the Lie group EKF (LG-EKF) for the MIMU/GNSS/magnetometer integrated navigation system. The corresponding measurement equations are also derived. A land vehicle field test has been conducted to evaluate the performance of EKF, ST-EKF (state transformation extended Kalman filter) and LG-EKF, which verifies LG-EKF's superior estimation accuracy of the heading angle as well as the other two horizontal angles (pitch and roll). The LG-EKF proposed in this paper is unlimited in the choice of sensors, which means it can be applied with both high-end and low-end inertial sensors.

Over the past few decades, the number of liquefied natural gas (LNG) ships and terminals has been increasing, playing an important role in global clean energy transportation. However, the traffic capacity of LNG shipping in port areas is limited because of its high safety requirements. In view of this problem, a novel model is proposed to study the ship traffic in a port area by combining cellular automaton (CA) and multi-agent methods. Taking the CNOOC Tianjin LNG Terminal as an example, the ship traffic in Tianjin Port is simulated. Based on the simulation results, the LNG ship traffic capacity and its impact on the general shipping traffic flow under different special traffic rules are obtained. This model can provide theoretical support for optimising the port traffic organisation for LNG ships.

Developing risk models to predict where collisions between vessels might occur is hindered by the relative sparsity of collisions. To address this, vessel encounters and near-misses have been used as a surrogate indicator of collision risk, referred to as ‘domain analysis’. When constructed empirically, using historical information, previous work is challenged by the multitude of factors which influence the passing distances between vessels. Within this paper, we conduct data mining of big vessel traffic datasets to determine the encounter characteristics across different waterways, vessel types and speeds, weather conditions and other exploratory variables. To achieve this, we utilise a novel approach of machine learning through a random forest algorithm to predict the critical passing distance between vessels in a multitude of conditions. We contribute a far greater range of influencing factors on domain size and shape than previous studies. Finally, we investigate the potential advantages of this approach to assess the spatial risk of collision across a large region. The results help to establish the factors that influence collision risk between navigating vessels and enable empirical maritime risk assessments.

‘Close-quarters situation’ is a term used in the International Regulations for Preventing Collisions at Sea. As the term is not precisely defined, this paper analyses the interpretations and definitions of the term by various authors or courts, based on judicial processes and judgments. In the end, the authors suggest their own definition of the term ‘close-quarters situation’. Knowing the minimum distance from another ship and the time to the closest point of approach at which collision may still be avoided by one's own manoeuvre is information that every ship's officer needs to know. In accordance with the proposed definition of the term ‘close-quarters situation’, minimum distances between ships and time to the closest point of approach in which the ship can still take action to avoid a collision by its own manoeuvring are determined by means of simulations on a navigational simulator. A total of 168 simulations were performed with three fine-form vessel sizes and three full-form vessel sizes. Due to the extensive amount of data, the paper presents the results for one vessel only.

To solve the problem of identifying ship model parameters quickly and accurately with the least test data, this paper proposes a nonlinear innovation parameter identification algorithm for ship models. This is based on a nonlinear arc tangent function that can process innovations on the basis of an original stochastic gradient algorithm. A simulation was carried out on the ship Yu Peng using 26 sets of test data to compare the parameter identification capability of a least square algorithm, the original stochastic gradient algorithm and the improved stochastic gradient algorithm. The results indicate that the improved algorithm enhances the accuracy of the parameter identification by about 12% when compared with the least squares algorithm. The effectiveness of the algorithm was further verified by a simulation of the ship Yu Kun. The results confirm the algorithm's capacity to rapidly produce highly accurate parameter identification on the basis of relatively small datasets. The approach can be extended to other parameter identification systems where only a small amount of test data is available.

This paper presents a novel estimation method for coarse alignment of a marine strapdown inertial navigation system (SINS) under mooring conditions. The properties of gravitational motion are used to improve the accuracy of coarse alignment. The parametric motion of gravitational apparent is a circle that is on the surface of a sphere. The location of this parametric circle is dependent on the definition of the reference frames and the initial angles of SINS. In the method proposed here the initial direct cosine matrix is calculated only from the location of the gravity motion parametric circle. The novelty of this paper is to provide a new method for estimating the gravity motion trajectory in inertial frame, as well as direct extraction of the initial direct cosine matrix from this estimated trajectory. Simulation and testing show that the proposed method is suitable for coarse alignment in mooring conditions.