Showing papers in "Journal of Navigation in 2021"

Ship detection from coastal surveillance videos via an ensemble Canny-Gaussian-morphology framework

Abstract: Abstract Coastal surveillance video helps officials to obtain on-site visual information on maritime traffic situations, which benefits building up the maritime transportation detection infrastructure. The previous ship detection methods focused on detecting distant small ships in maritime videos, with less attention paid to the task of ship detection from coastal surveillance video. To address this challenge, a novel framework is proposed to detect ships from coastal maritime images in three typical traffic situations in three consecutive steps. First the Canny detector is introduced to determine the potential ship edges in each maritime frame. Then, the self-adaptive Gaussian descriptor is employed to accurately rule out noisy edges. Finally, the morphology operator is developed to link the detected separated edges to connected ship contours. The model's performance is tested under three typical maritime traffic situations. The experimental results show that the proposed ship detector achieved satisfactory performance (in terms of precision, accuracy and time cost) compared with other state-of-the-art algorithms. The findings of the study offer the potential of providing real-time visual traffic information to maritime regulators, which is crucial for the development of intelligent maritime transportation.

Vessel Trajectory Prediction Using Historical Automatic Identification System Data

Abstract: For maritime safety and security, vessels should be able to predict the trajectories of nearby vessels to avoid collision. This research proposes three novel models based on similarity search of trajectories that predict vessels' trajectories in the short and long term. The first and second prediction models are, respectively, point-based and trajectory-based models that consider constant distances between target and sample trajectories. The third prediction model is a trajectory-based model that exploits a long short-term memory approach to measure the dynamic distance between target and sample trajectories. To evaluate the performance of the proposed models, they are applied to a real automatic identification system (AIS) vessel dataset in the Strait of Georgia, USA. The models' accuracies in terms of Haversine distance between the predicted and actual positions show relative prediction error reductions of 40·85% for the second model compared with the first model and 23% for the third model compared with the second model.

Augmented reality for maritime navigation data visualisation: a systematic review, issues and perspectives

Abstract: This study investigates the use of augmented reality technology (AR) in the field of maritime navigation and how researchers and designers have addressed AR data visualisation. The paper presents a systematic review analysing the publication type, the AR device, which information elements are visualised and how, the validation method and technological readiness. Eleven AR maritime solutions identified from scientific papers are studied and discussed in relation to previous navigation tools. It is found that primitive information such as course, compass degrees, boat speed and geographic coordinates continue to be fundamental information to be represented even with AR maritime solutions.

Lie group based nonlinear state errors for MEMS-IMU/GNSS/magnetometer integrated navigation

Abstract: 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.

Decision Tree and Logistic Regression Analysis to Explore Factors Contributing to Harbour Tugboat Accidents

Abstract: As tugboats interact very closely with ships in restricted waters, the possibility of accidents increases in these operations. Despite the high accident possibility, there is a gap in studies on tugboat accidents. This study aims to analyse accidents involving tugboats using data mining. For this purpose, a tugboat accidents dataset consisting of a total of 496 accident records for the period from 2008 to 2019 was collected. Logistic regression and decision tree algorithms were implemented to the dataset. The results revealed that tugboat propulsion type is the most important and influential factor in the severity of tugboat accidents. The inferences drawn from these results could be beneficial for tugboat operators and port authorities in enhancing their awareness of the factors affecting tugboat accidents. In addition, the outputs of this study can be a reference for management units in developing strategies for preventing tugboat accidents and can also be used in effective planning for practicable prevention programmes and practices.

Eye tracking in usability of electronic chart display and information system

Abstract: The purpose of this study is to test eye tracking in studying the usability of electronic chart display and information systems (ECDIS), one of the major components of ships’ bridge navigation systems. The carriage and adequate use of ECDIS on merchant ships is mandated by international regulations to improve maritime safety. The aim of this study is to test eye tracking as an assessment tool for usability of ECDIS. Eye movement data, collected from experienced port pilots operating on three different models of ECDIS, was analysed for the study. Significant differences were found between time to first fixation measurements among three different ECDIS models, as well as differences in heat map visualisations between the participant port pilots and expert users. The results indicate that eye tracking is an effective tool for assessment of the usability of ECDIS. The study, aiming potentially to improve the effectiveness of bridge navigation systems, proposes the integration of eye tracking in research and development of ECDIS, and contributes scientifically to research on eye tracking in marine transportation.

Effectiveness modelling of digital contact-tracing solutions for tackling the covid-19 pandemic

Abstract: Since the beginning of the coronavirus (COVID-19) global pandemic, digital contact-tracing applications (apps) have been at the centre of attention as a digital tool to enable citizens to monitor their social distancing, which appears to be one of the leading practices for mitigating the spread of airborne infectious diseases. Many countries have been working towards developing suitable digital contact-tracing apps to allow the measurement of the physical distance between citizens and to alert them when contact with an infected individual has occurred. However, the adoption of digital contact-tracing apps has faced several challenges so far, including interoperability between mobile devices and users’ privacy concerns. There is a need to reach a trade-off between the achievable technical performance of new technology, false-positive rates, and social and behavioural factors. This paper reviews a wide range of factors and classifies them into three categories of technical, epidemiological and social ones, and incorporates these into a compact mathematical model. The paper evaluates the effectiveness of digital contact-tracing apps based on received signal strength measurements. The results highlight the limitations, potential and challenges of the adoption of digital contact-tracing apps.

A prediction model of vessel trajectory based on generative adversarial network

Abstract: Trajectory prediction is an important support for analysing the vessel motion behaviour, judging the vessel traffic risk and collision avoidance route planning of intelligent ships. To improve the accuracy of trajectory prediction in complex situations, a Generative Adversarial Network with Attention Module and Interaction Module (GAN-AI) is proposed to predict the trajectories of multiple vessels. Firstly, GAN-AI can infer all vessels’ future trajectories simultaneously when in the same local area. Secondly, GAN-AI is based on adversarial architecture and trained by competition for better convergence. Thirdly, an interactive module is designed to extract the group motion features of the multiple vessels, to achieve better performance at the ship encounter situations. GAN-AI has been tested on the historical trajectory data of Zhoushan port in China; the experimental results show that the GAN-AI model improves the prediction accuracy by 20%, 24% and 72% compared with sequence to sequence (seq2seq), plain GAN, and the Kalman model. It is of great significance to improve the safety management level of the vessel traffic service system and judge the degree of ship traffic risk.

Seafarers’ awareness-based domain modelling in restricted areas

Abstract: Applying the existing domain model in ship domain research in a restricted area can be difficult owing to multiple factors that must be considered. This study presents a new domain model that can be applied in such environments. According to Endsley's theory of situation awareness, people have their own criteria in decision making based on factors such as individual and environmental factors. To investigate these factors, 125 seafarers were interviewed and threshold values based on their awareness were examined. The factors were evaluated as the closest points of approach. Domain modelling was performed based on these seafarer awareness values; then, the existing and awareness domains were compared, revealing that the awareness-based domain of seafarers is more affected by ship manoeuvrability than by ship size and speed, unlike the findings of existing domain research. Therefore, this study presents a new domain model that is both realistic and practical in a confined area, including various factors considered by seafarers based on the awareness values formed.

Path planning method for unmanned underwater vehicles eliminating effect of currents based on artificial potential field

Abstract: 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.

Evaluating the Latest Performance of Precise Point Positioning in Multi-GNSS/RNSS: GPS, GLONASS, BDS, Galileo and QZSS

Abstract: The single initial Global Positioning System (GPS) has been expanded into multiple global and regional navigation satellite systems (multi-GNSS/RNSS) as the Global Navigation Satellite System (GLONASS) is restored and the BeiDou Navigation Satellite System (BDS), Galileo Satellite Navigation System (Galileo) and Quasi-Zenith Satellite System (QZSS) evolve. Using the differences among these five systems, the paper constructs a consolidated multi-GNSS/RNSS precise point positioning (PPP) observation model. A large number of datasets from Multi-GNSS Experiment (MGEX) stations are employed to evaluate the PPP performance of multi-GNSS/RNSS. The paper draws three main conclusions based on the experimental results. (1) The combined GPS/GLONASS/Galileo/BDS/QZSS presents the PPP with the shortest mean convergence time of 11·5 min, followed by that of GPS/GLONASS/Galileo/BDS (12·4 min). (2) The combined GPS/GLONASS/BDS/Galileo/QZSS shows the optimal PPP performance when the cut-off elevation angle is basically the same because of the rich observation data due to a large number of satellites. To be specific, for combined GPS/GLONASS/BDS/Galileo/QZSS, the PPP convergence percentage is 80·9% higher relative to other combined systems under 35° cut-off elevation angle, and the percentages of the root mean square values of PPP within 0–5 cm are enhanced by 80·5%, 81·5% and 87·3% in the North, East and Up directions relative to GPS alone at 35° cut-off elevation angle. (3) GPS alone fails to conduct continuous positioning due to the insufficiency of visible satellites at 40° cut-off elevation angle, while the kinematic PPP of multi-GNSS/RNSS remains capable of obtaining positioning solutions with relatively high accuracy, especially in the horizontal direction.

Developing contextually aware ship domains using machine learning

Abstract: 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.

Identity recognition on waterways: a novel ship information tracking method based on multimodal data

Abstract: Abstract Video monitoring is an important means of ship traffic supervision. In practice, regulators often need to use an electronic chart platform to determine basic information concerning ships passing through a video feed. To enrich the information in the surveillance video and to effectively use multimodal maritime data, this paper proposes a novel ship multi-object tracking technology based on improved single shot multibox detector (SSD) and DeepSORT algorithms. In addition, a night contrast enhancement algorithm is used to enhance the ship identification performance in night scenes and a multimodal data fusion algorithm is used to incorporate the ship automatic identification system (AIS) information into the video display. The experimental results indicate that the ship information tracking accuracies in the day and night scenes are 78⋅2% and 70⋅4%, respectively. Our method can effectively help regulators to quickly obtain ship information from a video feed and improve the supervision of a waterway.

Consistency analysis of global positioning system position errors with typical statistical distributions

Abstract: Abstract Research into statistical distributions of φ, λ and two-dimensional (2D) position errors of the global positioning system (GPS) enables the evaluation of its accuracy. Based on this, the navigation applications in which the positioning system can be used are determined. However, studies of GPS accuracy indicate that the empirical φ and λ errors deviate from the typical normal distribution, significantly affecting the statistical distribution of 2D position errors. Therefore, determining the actual statistical distributions of position errors (1D and 2D) is decisive for the precision of calculating the actual accuracy of the GPS system. In this paper, based on two measurement sessions (900,000 and 237,000 fixes), the distributions of GPS position error statistics in both 1D and 2D space are analysed. Statistical distribution measures are determined using statistical tests, the hypothesis on the normal distribution of φ and λ errors is verified, and the consistency of GPS position errors with commonly used statistical distributions is assessed together with finding the best fit. Research has shown that φ and λ errors for the GPS system are normally distributed. It is proven that φ and λ errors are more concentrated around the central value than in a typical normal distribution (positive kurtosis) with a low value of asymmetry. Moreover, φ errors are clearly more concentrated than λ errors. This results in larger standard deviation values for φ errors than λ errors. The differences in both values were 25–39%. Regarding the 2D position error, it should be noted that the value of twice the distance root mean square (2DRMS) is about 10–14% greater than the value of R95. In addition, studies show that statistical distributions such as beta, gamma, lognormal and Weibull are the best fit for 2D position errors in the GPS system.

Automatic identification system data-driven model for analysis of ship domain near bridge-waters

Abstract: Abstract The ship safety domain plays a significant role in collision risk assessment. However, few studies take the practical considerations of implementing this method in the vicinity of bridge-waters into account. Therefore, historical automatic identification system data is utilised to construct and analyse ship domains considering ship–ship and ship–bridge collisions. A method for determining the closest boundary is proposed, and the boundary of the ship domain is fitted by the least squares method. The ship domains near bridge-waters are constructed as ellipse models, the characteristics of which are discussed. Novel fuzzy quaternion ship domain models are established respectively for inland ships and bridge piers, which would assist in the construction of a risk quantification model and the calculation of a grid ship collision index. A case study is carried out on the multi-bridge waterway of the Yangtze River in Wuhan, China. The results show that the size of the ship domain is highly correlated with the ship's speed and length, and analysis of collision risk can reflect the real situation near bridge-waters, which is helpful to demonstrate the application of the ship domain in quantifying the collision risk and to characterise the collision risk distribution near bridge-waters.

Exploring into contributing factors to young seafarer turnover: empirical evidence from China

Abstract: 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.

An improved PDR system with accurate heading and step length estimation using handheld smartphone

Abstract: Pedestrian dead reckoning (PDR) is widely used in handheld indoor positioning systems. However, low-cost inertial sensors built into smartphones provide poor-quality measurements, resulting in cumulative error which consists of heading estimation error caused by gyroscope and step length estimation error caused by an accelerometer. Learning more motion features through limited measurements is important to improve positioning accuracy. This paper proposes an improved PDR system using smartphone sensors. Using gyroscope, two motion patterns, walking straight or turning, can be recognised based on dynamic time warp (DTW) and thus improve heading estimation from an extended Kalman filter (EKF). Joint quasi-static field (JQSF) detection is used to avoid bad magnetic measurements due to magnetic disturbances in an indoor environment. In terms of periodicity of angular rate while walking, peak–valley angular velocity detection and zero-cross detection is combined to detect steps. A step-length estimation method based on deep belief network (DBN) is proposed. Experimental results demonstrate that the proposed PDR system can achieve more accurate indoor positioning.

Improved exponential weighted moving average based measurement noise estimation for strapdown inertial navigation system/doppler velocity log integrated system

Abstract: The strapdown inertial navigation system (SINS) with integrated Doppler velocity log (DVL) is widely utilised in underwater navigation. In the complex underwater environment, however, the DVL information may be corrupted, and as a result the accuracy of the Kalman filter in the SINS/DVL integrated system degrades. To solve this, an adaptive Kalman filter (AKF) with measurement noise estimator to provide noise statistical characteristics is generally applied. However, existing methods like moving windows (MW) and exponential weighted moving average (EWMA) cannot adapt to a dynamic environment, which results in unsatisfactory noise estimation performance. Moreover, the forgetting factor has to be determined empirically. Therefore, this paper proposes an improved EWMA (IEWMA) method with adaptive forgetting factor for measurement noise estimation. First, the model for a SINS/DVL integrated system is established, then the MW and EWMA based measurement noise estimators are illustrated. Subsequently, the proposed IEWMA method which is adaptive to the various environments without experience is introduced. Finally, simulation and vehicle tests are conducted to evaluate the effectiveness of the proposed method. Results show that the proposed method outperforms the MW and EWMA methods in terms of measurement noise estimation and navigation accuracy.

An improved nonlinear innovation-based parameter identification algorithm for ship models

Abstract: 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.

Key factors affecting the quality of maritime education and training: empirical evidence from China

Abstract: Recently, the quality of Maritime Education and Training (MET) has drawn more attention from employers of the shipping industry, because incompetent officers have frequently contributed to ship accidents. The authors intend to explore answers to three questions: (1) Is the quality of Chinese MET satisfactory to the maritime stakeholders? (2) What are the major factors affecting the quality of cadet training? (3) Are there feasible solutions to improve the quality of MET? The authors take China, a major seafarers supplying country, as empirical evidence to disclose the causes of the problems. By an exploratory factor analysis technique, this paper presents the findings of semi-structured interviews and questionnaire surveys and identifies four principal factors affecting the quality of MET in China. Shortage of skillful instructors, lack of onboard training, over-reliance on theoretical teaching, and limited funding sources are prominent factors in this context. Practical solutions are proposed for the purpose of improving the overall competence of Chinese cadets expected to become the dynamic workforce for the global shipping industry.

A cross-cultural investigation of design and visual preference of signage information from Hong Kong and Pakistan

Abstract: Signage design has been considered critically important for wayfinding, being a functional medium of delivering environmental information. Complex institutional environments have several factors affecting the wayfinding, including but not limited to the design of information signage and its visual preference. Visual preferences of information design in wayfinding signage vary, depending on the cultural and individual differences. This study explores the variance in design and visual preferences of wayfinding signage and its influencing elements. Responses through online questionnaire have been accumulated by the participants from Hong Kong and Pakistan based on their design and visual preference of campus wayfinding signage. Questions were asked related to the user preferences for signage colour if in line with the institutional visual identity, mono or multi-colour coding of information and its visual volume. In total, 170 university students and visitors participated in the exploratory study from the respective countries. The results demonstrated that participants of Hong Kong preferred inline colours of signage, along with mono or less colour coding and detailed information. While the other group preferred attractive colours with multi-colour coding and less detailed wayfinding information with pictograms. Individual differences concerning age, literacy level and gender were also computed, however trivial differences have been recorded. This study suggests the need for detailed cross-cultural investigation concerning elements of signage design and visual preference to identify the drivers for culturally consistent university signage.

A dynamic adaptive grating algorithm for AIS-based ship trajectory compression

Abstract: Automatic identification system (AIS)-based ship trajectory data are important for analysing maritime activities. As the data accumulate over time, trajectory compression is needed to alleviate the pressure of data storage, migration and usage. The grating algorithm, as a vector data compression algorithm with high compression performance and low computation complexity, has been considered as a very promising approach for ship trajectory compression. This algorithm needs the threshold to be set for each trajectory which limits the applicability over a large number of different trajectories. To solve this problem, a dynamic adaptive threshold grating compression algorithm is developed. In this algorithm, the threshold for each trajectory is dynamically generated using an effective approaching strategy. The developed algorithm is tested with a complex trajectory dataset from the Qiongzhou Strait, China. In comparison with the traditional grating method, our algorithm has improved advantages in the ease of use, the applicability to different trajectories and compression performance, all of which can better support relevant applications, such as ship trajectory data storage and rapid cartographic display.

AIS-based near-collision database generation and analysis of real collision avoidance manoeuvres

Abstract: Abstract Economic and technological development has increased the amount, density and complexity of maritime traffic, which has resulted in new challenges. One challenge is conforming to the distinct evasion manoeuvres required by vessels entering into near-collision situations (NCSs). Existing rules are vague and do not precisely dictate which, when and how collision avoidance manoeuvres (CAMs) should be executed. The automatic identification system (AIS) is widely used for vessel monitoring and traffic control. This paper presents an efficient, scalable method for processing large-scale raw AIS data using the closest point of approach (CPA) framework. NCSs are identified to create a database of historical traffic data. Important features describing CAMs are defined, estimated and analysed. Applications on a high-quality real-world data set show promising results for a subset of the identified situations. Future applications may play a significant role in the maritime regulatory framework, navigation protocol compliance evaluation, risk assessment, automatic collision avoidance, and algorithm design and testing for autonomous vessels.

Precise Single-Frequency Positioning Using Low-Cost Receiver with the Aid of Lane-Level Map Matching for Land Vehicle Navigation

Abstract: Precise positioning with low-cost single-frequency global navigation satellite system (GNSS) receivers has great potential in a wide range of applications because of its low price and improved accuracy. However, challenges remain in achieving reliable and accurate solutions using low-cost receivers. For instance, the successful ambiguity fixing rate could be low for real-time kinematic (RTK) while large errors may occur in precise point positioning (PPP) in some scenarios (e.g., trees along the road). To solve the problems, this paper proposes a method with the aid of additional lane-level digital map information to improve the accuracy and reliability of RTK and PPP solutions. In the method, a digital camera will be applied for lane recognition and the positioning solution from a low-cost receiver will be projected to the digital map lane link. With the projected point position as a constraint, the RTK ambiguity fixing rate and PPP performance can be enhanced. A field kinematic test was conducted to verify the improvement of the RTK and PPP solutions with the aid of map matching. The results show that the RTK ambiguity fixing rate can be increased and the PPP positioning error can be reduced by map matching.

A cellular automaton-based model of ship traffic flow in busy waterways

Abstract: In busy waterways, spatial-temporal discretisation, safe distance and collision avoidance timing are three of the core components of ship traffic flow modelling based on cellular automata. However, these components are difficult to determine in ship traffic simulations because the size, operation and manoeuvrability vary between ships. To solve these problems, a novel traffic flow model is proposed. Firstly, a spatial-temporal discretisation method based on the concept of a standard ship is presented. Secondly, the update rules for ships’ motion are built by considering safe distance and collision avoidance timing, in which ship operation and manoeuvrability are thoroughly considered. We demonstrate the effectiveness of our model, which is implemented through simulating ship traffic flow in a waterway of the Yangtze River, China. By comparing the results with actual observed ship traffic data, our model shows that the behaviours and the characteristics of ships’ motions can be represented very well, which also can be further used to reveal the mechanism that affects the efficiency and safety of ship traffic.

Modelling liquefied natural gas ship traffic in port based on cellular automaton and multi-agent system

Abstract: 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.

Compressed pseudo-SLAM: pseudorange-integrated compressed simultaneous localisation and mapping for unmanned aerial vehicle navigation

Abstract: 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

Wireless power charging of drone using vision-based navigation

Abstract: 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+).