Safe Navigation Research Articles

Multi-Feature-Filtering-Based Road Curb Extraction from Unordered Point Clouds

Road curb extraction is a critical component of road environment perception, being essential for calculating road geometry parameters and ensuring the safe navigation of autonomous vehicles. The existing research primarily focuses on extracting curbs from ordered point clouds, which are constrained by their structure of point cloud organization, making it difficult to apply them to unordered point cloud data and making them susceptible to interference from obstacles. To overcome these limitations, a multi-feature-filtering-based method for curb extraction from unordered point clouds is proposed. This method integrates several techniques, including the grid height difference, normal vectors, clustering, an alpha-shape algorithm based on point cloud density, and the MSAC (M-Estimate Sample Consensus) algorithm for multi-frame fitting. The multi-frame fitting approach addresses the limitations of traditional single-frame methods by fitting the curb contour every five frames, ensuring more accurate contour extraction while preserving local curb features. Based on our self-developed dataset and the Toronto dataset, these methods are integrated to create a robust filter capable of accurately identifying curbs in various complex scenarios. Optimal threshold values were determined through sensitivity analysis and applied to enhance curb extraction performance under diverse conditions. Experimental results demonstrate that the proposed method accurately and comprehensively extracts curb points in different road environments, proving its effectiveness and robustness. Specifically, the average curb segmentation precision, recall, and F1 score values across scenarios A, B (intersections), C (straight road), and scenarios D and E (curved roads and ghosting) are 0.9365, 0.782, and 0.8523, respectively.

Exploring the Use of Fengyun-3 Meteorological Satellite for Monitoring Sea Ice to Provide Services for Polar Navigation

This paper explores the feasibility and effectiveness of using the Fengyun-3 meteorological satellite to monitor sea ice, providing services for ships navigating in polar regions. Firstly, it analyzes the impact of Arctic sea ice changes on ship navigation and the importance of sea ice monitoring in route planning. Next, it provides a detailed introduction to the data sources and processing methods of the Fengyun-3 satellite, including radiometric calibration, geometric correction, image registration, and cropping. Subsequently, it discusses the characteristics of sea ice in the visible spectrum and successfully extracts sea ice information using MERSI-II data with land, cloud, and seawater masking techniques. The study indicates that the comprehensive use of multi-spectral data and other observation methods can significantly enhance sea ice monitoring capabilities. In the future, integrating more advanced technologies is expected to achieve refined identification and short-term prediction of sea ice movement, thereby providing more scientific and efficient support for ships navigating in polar regions, enhancing navigation safety and efficiency, and offering a scientific basis for the development of Arctic shipping routes.

Research on Ship Data Analysis Platform Development and Its Application in Teaching

This paper delves into the significant importance of developing a ship data analysis platform, which aims to enhance the operational efficiency and safety of the shipping industry by integrating big data processing technologies. The development of the platform centers on crucial aspects such as data collection, preprocessing, analysis, and visualization, enabling real-time monitoring and intelligent prediction of ship operational status. This platform not only assists in optimizing route planning and reducing energy consumption costs but also provides robust support for navigational safety. In project-based teaching, this platform serves as a bridge between theory and practice, cultivating students' abilities in big data processing and analysis through case studies, practical exercises, and other forms, thereby promoting the cultivation of high-quality talents and technological innovation in the field of shipping.

Efficient 3D robotic mapping and navigation method in complex construction environments

AbstractRecent advancements in construction robotics have significantly transformed the construction industry by delivering safer and more efficient solutions for handling complex and hazardous tasks. Despite these innovations, ensuring safe robotic navigation in intricate indoor construction environments, such as attics, remains a significant challenge. This study introduces a robust 3‐dimensional (3D) robotic mapping and navigation method specifically tailored for these environments. Utilizing light detection and ranging, simultaneous localization and mapping, and neural networks, this method generates precise 3D maps. It also combines grid‐based pathfinding with deep reinforcement learning to enhance navigation and obstacle avoidance in dynamic and complex construction settings. An evaluation conducted in a simulated attic environment—characterized by various truss structures and continuously changing obstacles—affirms the method's efficacy. Compared to established benchmarks, this method not only achieves over 95% mapping accuracy but also improves navigation accuracy by 10% and boosts both efficiency and safety margins by over 30%.

Research on Collision Warning Method for Ship-Bridge Based on Safety Potential Field

In order to ensure the safety of navigation in a bridge area, and based on the theory of the safety potential field, a method of ship safety assessment and early warning in an inland river bridge area is proposed. Firstly, the risk elements associated with ship collisions in a bridge area are classified. Secondly, these risks are quantified using the potential energy field, the boundary potential field and the behavioural field, and then the ship state under the influence of wind flow, predicted by the Kalman filter, is quantified using the kinetic energy field. Then, the above four potential energy fields are merged to obtain a superposition field, and the magnitude of the instantaneous risk in the bridge area is obtained based on its magnitude. Finally, the change of field strength values under different moments is used for early warning. The results of the simulation of a ship passing through the piers of the Baijusi Bridge show that the model can effectively quantify the risk of a ship–bridge collision in the inland bridge area and provide real-time warning of the risk of a ship–bridge collision in the bridge area, which is of great significance for improving the safety of the inland bridge area.

Addressing unpredictable movements of dynamic obstacles with deep reinforcement learning to ensure safe navigation for omni-wheeled mobile robot

The safe and efficient navigation of mobile robots in the presence of unknown dynamic obstacles remains a complex and unresolved challenge. This paper presents collision-free path planning for a mobile robot that safely deals with multi-directional obstacles, that is, randomly moving dynamic obstacles, using a Deep Reinforcement Learning (DRL) algorithm named Deep Q-Network (DQN) with inflated robot reward functions. The robot moves in a time-efficient and collision-free route while maintaining a safe distance with both static and unpredictable dynamic obstacles. The modified DQN algorithm takes RGB images of the environment as input for training a Convolution Neural Network (CNN) and provides a safe and short path for navigation. The robot used for training is an omni-wheeled mobile robot exploring outdoors, that is, concourse environment, and indoors, that is, home environment. The Closed-Loop Inverse Kinematics (CLIK) algorithm is employed to control a mobile robot to follow the desired path. The simulation results indicate that the proposed algorithm with inflated robot reward functions demonstrates remarkable performance as compared to recently used Reinforcement Learning (RL) algorithms when dealing with both stationary and randomly moving obstacles in the given environment.

A Risk Identification Method for Ensuring AI-Integrated System Safety for Remotely Controlled Ships with Onboard Seafarers

The maritime sector is increasingly integrating Information and Communication Technology (ICT) and Artificial Intelligence (AI) technologies to enhance safety, environmental protection, and operational efficiency. With the introduction of the MASS Code by the International Maritime Organization (IMO), which regulates Maritime Autonomous Surface Ships (MASS), ensuring the safety of AI-integrated systems on these vessels has become critical. To achieve safe navigation, it is essential to identify potential risks during the system planning stage and design systems that can effectively address these risks. This paper proposes RA4MAIS (Risk Assessment for Maritime Artificial Intelligence Safety), a risk identification method specifically useful for developing AI-integrated maritime systems. RA4MAIS employs a systematic approach to uncover potential risks by considering internal system failures, human interactions, environmental conditions, AI-specific characteristics, and data quality issues. The method provides structured guidance to identify unknown risk situations and supports the development of safety requirements that guide system design and implementation. A case study on an Electronic Chart Display and Information System (ECDIS) with an AI-integrated collision avoidance function demonstrates the applicability of RA4MAIS, highlighting its effectiveness in identifying specific risks related to AI performance and reliability. The proposed method offers a foundational step towards enhancing the safety of software systems, contributing to the safe operation of autonomous ships.

Letting losses be lessons: Human-machine cooperation in maritime transport

Navigation safety has been a critical guarantee of global shipping, and it becomes more challenging given the increasing employment of advanced technologies and novel ship design in the era of Maritime Autonomous Surface Ships (MASS). The human-centred risk analysis of human-machine cooperation is scarce in general and emerging in maritime transport in specific. This paper aims to develop a new approach enabling the analysis of significant risk influencing factors (RIFs) in human-machine cooperation through an in-depth investigation of the occurred mistakes and violations in the cooperative operations of seafarers and machines in maritime transport. Its novelties consist of (1) a novel approach to analysing and quantifying the connectivity between humans and machines in safety-critical operations, (2) new integration of the frequency and impact of RIFs in the human-machine cooperation model, and (3) ultilisation of graph theory to generate a network to analyse critical human-machine RIFs and their interactions with the system. The connectivity analysis of RIFs is conducted through a weighted undirected network, showing the features of RIF connectivity accommodating the closed-loop system. The proposed novel approach, which combines the frequency and impact features to identify critical RIFs and analyses graphical features, will aid to realise the human-centred risk analysis for MASS. The findings make contributions for ship designers to rationalise the clustering design of function-based automation and training organisations to improve seafarer skills by rationally considering the identified risk-based human-machine cooperation features, and providing new competence schemes that can fit the demands of MASS in future.

A knowledge graph-based inspection items recommendation method for port state control inspection of LNG carriers

The safe navigation and operation of Liquified natural gas (LNG) carriers is crucial in maritime transportation. The Port State Control (PSC) inspection is the primary method for identifying deficiencies in vessels. Different from other vessels, the PSC inspection of LNG carriers requires knowledge of specialized transport equipment. Therefore, effective management of inspection knowledge and precise targeting of inspection items are particularly crucial. Knowledge graph is an efficient way to manage knowledge in the context of artificial intelligence. In this study, an LNG carrier PSC inspection knowledge graph is constructed for fusing multisource knowledge data from PSC inspection. Additionally, a knowledge graph-based recommendation model, namely the improved knowledge graph convolutional network combined with pretrained translation embedding (PT-KGCN), is developed to recommend inspection items and knowledge. The PT-KGCN model first carries out knowledge graph embedding. It then predicts possible deficiencies on the basis of historical PSC inspection data. Finally, it recommends inspection items by correlating the predicted deficiencies with knowledge from the knowledge graph. The results show that more than 87% of defective items in historical data are correctly predicted. The research findings can provide ideas for the practical application of knowledge data in the inspection fields.

Merging the Application of Artificial Intelligence Technology in Maritime Industry: A Systematic Literature Review

This article presents a thorough literature review on the use of technology in artificial intelligence technology within the maritime industry. The aim is to analyse existing research and identify crucial themes, theoretical frameworks, methodologies, and areas of research gaps in this field. Specific keywords related to artificial intelligence and the maritime industry were used to search the Web of Science (WoS) and Scopus databases. A list of the 34 most frequently cited articles was compiled and analysed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure a systematic and rigorous approach to selecting relevant manuscripts. The analysis revealed three key themes: (1) Role of Artificial Intelligence technology in the maritime industry, (2) challenges in adapting Artificial Intelligence technology in the maritime industry and (3) digitalisation and smart shipyards. Additionally, navigational safety and environmental impact were identified as important considerations in this field. Integrating artificial intelligence into maritime organisations able to develop immersive and impactful syllabus that simulate advanced technology. This allows maritime practitioners to develop related skills and better understand advanced technology. Furthermore, this integration fosters growth and success in the maritime industry, preparing for future development.

The Share of Data in Maritime Communications is Increasing

Communication is of great importance for the safe and delay-free navigation of ships. Especially life/property safety, navigational warnings, weather forecast reports, storm warnings, navigation maps, etc. Timely transmission of information to ships is only possible with effective maritime communications. In addition, communication is of great importance in making appropriate navigation plans based on the information received from the ships. In recent years, the share of data communication in individual and corporate communications has been increasing. In order to benefit from the great conveniences and opportunities provided by data communication in maritime communication systems, very radical decisions have recently been taken within IMO. These regulations made at the international level paved the way for data communication in close-range maritime communication systems. In line with these regulations, broadband data communication between the ship and the land will begin to be made via VHF devices in the next few years. Another development that will improve broadband data communication in maritime communication systems has been in Near Orbit Satellite systems. The technical features of these systems, the number of which has been increasing in recent years, are extremely suitable for data communication between ship and land. Among these, the Starlink system completes its network in space faster than other satellite systems. It is inevitable that the Starlink system, whose global satellite structure will be completed to a large extent by the end of 2025, will be used to a large extent in data communication between ships and land and between ships with each other. With the use of broadband data feature in the near future, it will be possible for ships to navigate much more safely, efficiently and with a reduced risk of accidents. For this purpose, maritime enterprises, ship personnel and land units must be informed about these developments and their communication infrastructure must be established by taking these systems into consideration.

Understanding of the Maritime Future Mentality; Safe E-navigation and Safe Maritime Surface Communication

Developing and changing technology affects all sectors globally. Although it primarily affects information systems digitally, it affects all sectors indirectly. Maritime transport, the most important transportation mode in the world, is affected by technological progress as seafarers, ships, and ports. When used for its intended purpose, the technology employs intelligent and rational solutions based on the logic of identifying previous errors and developing predictions accordingly. Maritime transportation is the movement of ships between ports safely and without harming the environment. The sea is a dynamic surface not previously exposed to fixed effects and is affected by meteorological and environmental conditions. As the international maritime authorities keep pace with technological advancements, they have embraced the e-navigation concept, a digital revolution that is set to transform the industry. This shift to Electronic Navigation requires all operations to be digital, making transmission easier and more efficient. It also mandates uninterrupted and high-quality digital communication with ships' land facilities during the entire voyage. ECDIS, one of the advanced automation technology products used for e-navigation, and the vector map ENC it uses are of great importance. ENC maps are produced with specific standards. S-100, which is described as the latest and most advanced standard, provides sailors with good opportunities for safe navigation and communication. The study has been prepared to explain e-navigation types of equipment, their standards, and how they communicate according to cyber security.

An evaluation of cyber-worthiness and related factors in maritime safety and maritime security

ABSTRACT This article revisits the traditional definition and concept of seaworthiness against the backdrop of technological developments in maritime transport and maritime law. These developments in the maritime industry, coupled with the increasing volume of maritime commerce, have affected the traditional doctrine of seaworthiness and necessitate its redefinition. Of the many developments, this article focuses on maritime security generally and cyber-risk and hence cyber-worthiness in particular. The article argues that cyber risk management, as a part of maritime safety and security standards, should be reflected in the modern doctrine of seaworthiness. It is problematic to provide a generally acceptable uniform definition for seaworthiness in maritime law, because the concept is both relative and technical. Furthermore, and considering the components of a seaworthy ship, the need to assess new systems related to technology used in ships and safety in navigation, and to determine whether a ship can be deemed “cyber-secure” has become essential.

Ship model-based route optimisation for decision support in deep sea shipping

Abstract We present a new approach and route optimization methodology to support analysis and planning of vessel and fleet performance in deep sea shipping for green, energy efficient, and safe navigation. The developed methodology combines the ship technical characteristics based on a ship model developed for a particular vessel type and energy-saving technology options and an optimization algorithm taking into account weather conditions. Optimization involves two stages: graph construction and Dynamic Programming labelling algorithm implemented to solve the shortest path problem with variable speed. The new approach is implemented as part of a decision-support tool EcoRouter enabling the user to conduct analysis of safe and Pareto optimal solutions. Several applications to support real fleet planning and ship performance analysis have been identified including project engineering for future energy-saving ship technologies, for example, wind-assisted propulsion.

Unveiling risk factors influencing the selection of the Northern Sea Route: A conjoint analysis approach for Japanese shippers

This paper explores how Japanese corporations assess risks associated with using the Northern Sea Route (NSR) as a new maritime transport route in Asia and identifies measures to promote its usage. We address two key questions: (1) What risks influence shippers' route choices in maritime transport? (2) Does risk evaluation vary by cargo type and company size? Using choice-based conjoint analysis and a conditional logit model with online survey data, we identified five main risk factors influencing shippers: route unavailability rate, delay probability, piracy probability, transport costs, and war conditions, ranked in importance from highest to lowest as war conditions, delay probability, transport costs, route unavailability rate, and piracy probability. Additionally, the risk factors significantly influencing route selection were found to be robust, showing minimal variation across company size, type of trade (import/export), and the nature of the goods transported. Our findings suggest several policy implications. Diplomatic efforts are crucial for safe vessel navigation on the NSR. Government initiatives should focus on reducing transport delays through technology investments and implementing strategies to decrease NSR delay rates without increasing shippers' costs. Additionally, emphasizing the lower piracy risks on the NSR compared to Suez Canal Route could boost its attractiveness.

UAV path planning based on improved dung beetle algorithm with multiple strategy integration

This research presents a multi-strategy augmented dung beetle algorithm (CDBO) for UAV path planning in intricate 3D environments. Its main objective is to address the path planning challenge by formulating an integrated cost function model and environment representation that aligns the optimisation task with the navigation prerequisites and safety constraints of the UAV. Firstly, the algorithm initialises the particle population based on the Lévy flight principle to improve the species diversity and adequately search the solution space. Subsequently, an exponentially decreasing inertia weighting strategy is introduced to improve the convergence speed of the algorithm. In addition, the algorithm uses an adaptive [Formula: see text]-distribution with perturbed variation of the updated positions so that the algorithm avoids falling into a local optimum. Finally, to enhance the robustness of the system, the algorithm incorporates an elite retention strategy based on the Pareto principle. In this study, rigorous tests using 12 CEC2022 test functions and Wilcoxon rank sum test are conducted as benchmarks for the performance of the algorithm. The results show that the enhanced dung beetle algorithm outperforms the traditional algorithm, highlighting its efficiency and robustness. In addition, the efficacy of the enhanced dung beetle algorithm was validated in a variety of complex 3D environments, affirming its ability to generate optimal paths. This study further highlights the importance of effective algorithms in UAV path planning, addressing the key issue of optimising flight operations in intricate spatial scenarios.

Navigating Urban Skies: Obstacle Avoidance Strategies for Quadrotor MAVs

Abstract Micro Aerial Vehicles (MAVs) has gained attentions since more than two decades ago starting from the applications in air combat up to civil applications such as packet deliveries, environmental monitoring, and surveillance. In an environment such as cities that grows denser, navigation and control for drones becomes challenging to ensure safe navigation around buildings and other obstacles. This study proposes an approach for obstacle avoidance for MAVs by using ultrasonic sensors. Four sensors are strategically positioned to cover the front, right, back, and left directions. Additionally, a downward-facing sensor measures the quadrotor’s height above ground. Our goal is to develop autonomous MAV that can avoid obstacles, ensuring safe flight even in complex urban landscapes. The scenario implemented in the study is by introducing obstacle in any directions. When an obstacle is detected by the ultrasonic sensor, the signal will be sent to microcontroller and the attitude of the MAVs, roll or pitch will be adjusted to avoid the obstacle by moving to the counter direction of the obstacle. We conducted 20 trials of experiments by varying the gain values of Proportional Integral Derivative (PID) values, we fine-tune our obstacle avoidance algorithm. Modifications include optimizing roll and pitch adjustments, refining detection height thresholds, and implementing countermeasures after obstacle clearance. The results show that our proposed method has 10% overshoot when detecting any obstacles in different directions to avoid the obstacles. Our findings contribute to the advancement of safe and efficient urban drone operations, bridging the gap between technology and real-world challenges.

Dynamic Scene Representation for Docking in Urban Waters Using a Stereo Camera

Abstract To navigate and dock safely in urban waters, an autonomous ferry must assess its safe navigation paths by identifying navigable free space and potential obstacles. A deep understanding of its surroundings is crucial for situational awareness. Knowing which objects are static and dynamic is an indispensable ingredient for path planning algorithms and collision avoidance systems, and is especially useful for docking operations, as moving obstacles can interfere with the optimal path to the dock. To handle all types of obstacles, we present a novel dynamic scene representation for docking in urban waters using a short baseline stereo camera. Obstacles protruding from the water surface are represented with vertically oriented rectangles, known as stixels. The stixels are tracked over consecutive frames with dense optical flow. Together with stereo correspondences, their 3D motion is estimated. Stixels representing the same object are grouped together into line segments which are classified as either dynamic or static based on the motion of its associated stixels. Our dynamic representation presents objects in the 3D camera coordinate system as a bird’s eye view map. Additionally, stereo depth uncertainty can significantly impact depth estimates and is considered using a Kalman filter, providing smooth position and velocity estimates. We test our dynamic scene representation on a real docking scenario recorded with an autonomous ferry using a stereo camera. We evaluate the accuracy of the reconstructed 3D scene points and the velocity estimates of the dynamic objects using LiDAR and GNSS.

Research on the relationships between river erosion and sedimentation and energy loss in the Yangtze River Basin of China

River scour and siltation, inherently prolonged and multifaceted phenomena, profoundly impact the hydraulic conductivity and navigational safety of waterways, thereby rendering the precise forecasting of their fluctuations a paramount and unresolved challenge. This study embarks on a novel investigative trajectory by examining energy dissipation in rivers, specifically through the scrutiny of empirical hydrological records. Employing the principles of energy balance theory, this study elucidates the intricate connection between energy losses and sedimentation volumes in the upper reaches of the Yangtze River and at its confluence with the significant tributary, the Jialing River.Our findings diverge from conventional wisdom, revealing that standard metrics of energy loss, encompassing kinetic, potential, and total mechanical energies, along with momentum losses, inadequately characterize the complexity of sediment deposition processes. In contrast, we demonstrate that the loss of suspension power is a more indicative measure of sedimentation quantity. Through meticulous comparisons of various formulations for calculating suspension power, we identified a formula grounded in the concept of minimal available power dissipation in river systems that exhibits superior accuracy in correlating suspension power losses with sedimentation patterns in river sections. This formula's efficacy is further enhanced when considering aggregate suspension power dissipation under the influence of tributary inflows, enabling refined predictions of sedimentation variations along river segments. In conclusion, the insights derived from this meticulous research contribute significantly to the realm of river resource management and the pursuit of sustainable, high-quality aquatic ecosystem development.

Method for Extracting Inland Unmanned Vessel Routes Based on AIS Data

This study introduces a novel approach to enhance inland navigation safety and optimize unmanned vessel routes using AIS data. The trajectory partition algorithm categorizes Yangtze River trajectory data to formulate round-trip routes. A turning point identification algorithm contributes to recognizing crucial turning points, followed by clustering using the HDBSCAN algorithm. Cluster centroids extracted from these clusters serve as essential waypoints for route planning. The Akima interpolation polynomial is applied judiciously to interpolate waypoints, culminating in meticulous route generation. Validation employs a dataset of 5,480,049 dynamic trajectory points from the Yangtze River, demonstrating the method's efficacy. Results indicate mean squared errors of 0.77% and 6.21%, symmetrical mean absolute percentage errors of 5.3% and 7.3%, and correlation coefficients of 99.62% and 97.14% with actual routes, respectively. This method adeptly extracts inland vessel round-trip routes, aligning seamlessly with stringent criteria for accuracy, effectiveness, and applicability.