Search Results (51)

Autonomous ship navigation systems must ensure safe and efficient route planning while complying with the International Regulations for Preventing Collisions at Sea (COLREGs). This paper presents an integrated navigation framework that combines chart-based global path planning with a Model Predictive Control (MPC) approach for local trajectory tracking and COLREG-compliant collision avoidance. The method generates feasible reference routes using maritime charts and predefined waypoints, while the MPC controller ensures precise path following and dynamic re-planning in response to nearby vessels and coastal obstacles. Coastal features and shorelines are modeled using Global Self-consistent, Hierarchical, High-resolution Geography data, enabling MPC to treat landmasses as static obstacles. Other vessels are represented as dynamic obstacles with varying speeds and headings, and COLREG rules are embedded within the MPC framework to enable rule-compliant maneuvering during encounters. To address real-time computational constraints, a simplified MPC formulation is introduced, balancing predictive accuracy with computational efficiency, making the approach suitable for embedded implementations. The navigation framework is implemented in a MATLAB-based simulation with real-time visualization supporting multi-vessel scenarios and COLREG-aware vessel interactions. Simulation results demonstrate robust performance across diverse maritime scenarios—including complex multi-ship encounters and constrained coastal navigation—while maintaining the shortest safe routes. By seamlessly integrating chart-aware path planning with COLREG-compliant, MPC-based collision avoidance, the proposed framework offers an effective, scalable, and robust solution for autonomous maritime navigation. Full article

Seaports, as major transportation hubs, generate significant air pollution due to intensive ship traffic, directly affecting local air quality. While emission inventories are commonly used to manage ship-based air pollution, they reflect only the emission-related aspect of a specified period and area, limiting the broader interpretability and comparability of the results. To overcome the mentioned challenges, this research presents the PrE-PARE model, which enables the prediction, analysis, and risk evaluation of ship-sourced air pollution in port areas. The model comprises three interconnected modules. The first is applied for quantifying emissions using detailed technical and movement datasets, which are combined into individual voyage trajectories to enable a high-resolution analysis of ship-based air pollutants. In the second module, the Multivariate Adaptive Regression Splines (MARS) machine learning method is adapted to predict emissions in varying operational scenarios. In the third module, novel metric methods are introduced, enabling a standardised efficiency comparison between ships. These methods are supported by a unique classification system to determine the emission risk in different periods, evaluate the intensity of various ship types, and rank individual ships based on their operational efficiency and emission optimisation potential. By combining new methods with technical and operational shipping data, the model provides a transparent, comparable, and adaptable system for emissions monitoring. The results demonstrate that the PrE-PARE model has the potential to improve strategic planning and air quality management in ports while remaining flexible enough to be applied in different contexts and future scenarios. Full article

In dynamic environments with continuous variability, such as those affected by typhoons, ship path planning must account for both navigational safety and the maneuvering characteristics of the vessel. However, current methods often struggle to accurately capture the continuous evolution of dynamic obstacles and generally lack adaptive exploration mechanisms. Consequently, the planned routes tend to be suboptimal or incompatible with the ship’s maneuvering constraints. To address this challenge, this study proposes a Space–Time Integrated Q-Learning (STIQ-Learning) algorithm for dynamic path planning under typhoon conditions. The algorithm is built upon the following key innovations: (1) Spatio-Temporal Environment Modeling: The hazardous area affected by the typhoon is decomposed into temporally and spatially dynamic obstacles. A grid-based spatio-temporal environment model is constructed by integrating forecast data on typhoon wind radii and wave heights. This enables a precise representation of the typhoon’s dynamic evolution process and the surrounding maritime risk environment. (2) Optimization of State Space and Reward Mechanism: A time dimension is incorporated to expand the state space, while a composite reward function is designed by combining three sub-reward terms: target proximity, trajectory smoothness, and heading correction. These components jointly guide the learning agent to generate navigation paths that are both safe and consistent with the maneuverability characteristics of the vessel. (3) Priority-Based Adaptive Exploration Strategy: A prioritized action selection mechanism is introduced based on collision feedback, and the exploration factor $ϵ$ is dynamically adjusted throughout the learning process. This strategy enhances the efficiency of early exploration and effectively balances the trade-off between exploration and exploitation. Simulation experiments were conducted using real-world scenarios derived from Typhoons Pulasan and Gamei in 2024. The results demonstrate that in open-sea environments, the proposed STIQ-Learning algorithm achieves reductions in path length of 14.4% and 22.3% compared to the D* and Rapidly exploring Random Trees (RRT) algorithms, respectively. In more complex maritime environments featuring geographic constraints such as islands, STIQ-Learning reductions of 2.1%, 20.7%, and 10.6% relative to the DFQL, D*, and RRT algorithms, respectively. Furthermore, the proposed method consistently avoids the hazardous wind zones associated with typhoons throughout the entire planning process, while maintaining wave heights along the generated routes within the vessel’s safety limits. Full article

This paper tackles the highly challenging problem of automatic berthing for autonomous surface vessels (ASVs), encompassing trajectory planning, trajectory tracking, and collision avoidance. Firstly, a novel A* algorithm integrated with a quasi-uniform B-spline and quadratic interpolation method (A*QB) is proposed for generating a smooth trajectory from the initial position to the berth, utilizing an offline-generated scaled map. Secondly, the optimal nonlinear model predictive control (NMPC)-based trajectory-tracking framework is established, incorporating the model’s uncertainty, the input saturation, and environmental disturbances, based on a 3-DOF model of a ship. Finally, considering the collision risks during port berthing, a COLREGs-based collision avoidance method is investigated. Consequently, a novel trajectory-tracking and COLREGs-based collision avoidance (TTCCA) scheme is proposed, ensuring that the ASV navigates along the desired trajectory, safely avoids both static and dynamic obstacles, and successfully reaches the berth. To validate the TTCCA approach, numerical simulations are conducted across four scenarios with comparisons to existing methods. The experimental results demonstrate the effectiveness and superiority of the proposed scheme. Full article

Maintenance of offshore wind farms requires the transportation of tools and spare parts in close coordination with the deployment of technicians and the cost-intensive shutdown of the wind turbines. In addition to ships and helicopters, drones are envisioned to support the offshore transportation system in the future. For cost-efficient and scalable offshore drone operations, autonomy is key to minimize the required infrastructure and personnel. In this work, we present a system architecture that integrates the key onboard capabilities for autonomous offshore drone operations: onboard mission and contingency management, en-route trajectory planning, robust flight control, safe landing, communication management, and runtime monitoring. We also present technical solutions for each of these capabilities and discuss their integration and interaction within the autonomy architecture. Furthermore, remaining challenges and the feasibility of autonomous drone operations for offshore wind farm cargo delivery are addressed, contributing to the realization of this vision in the near future. The work presented here summarizes the results of autonomous cargo drone operations within the UDW research project, a joint project between the German Aerospace Center (DLR) and the energy supplier EnBW. Full article

Sea transportation plays a vital role in global trade, and studying the impact of emergencies on global sea transportation is essential to ensure the stability of trade. At present, the conflict between Palestine and Israel has attracted extensive attention worldwide. However, there is a lack of specific research on the impact of conflict on shipping, particularly on global shipping costs. By using the global vessel trajectory data of tankers from the Automatic Identification System (AIS) and taking the global sea transportation of large tankers as an example, this paper quantifies and visualizes the changes in global sea transportation before and after conflicts from a data-driven perspective. Firstly, the complete vessel trajectory, as well as the port of departure and the port of destination are extracted. Then, from the perspective of shipping cost and vessel traffic flow, we evaluate the vessel traffic flow changes caused by the conflict by using the route distance to replace the shipping costs and quantify the cost increase for the relevant countries caused by the vessel detour based on the shipping cost increment index. The research results show that after the outbreak of the conflict, the number of vessels passing through the Red Sea area has decreased significantly. About 3.1% of global vessels were affected, with global sea transportation costs of large tankers increasing by about 0.0825%. This study takes the Israeli–Palestinian conflict as an example and analyzes the impact of emergencies on the global sea transportation situation of tankers based on AIS data. The research results reveal the characteristics of international shipping to a certain extent and provide guidance for global sea transportation route planning. Full article

A hybrid probabilistic road map (PRM) path planning algorithm based on historical automatic identification system (AIS) information and Douglas–Peucker (DP) compression is proposed to address the issues of low path quality and the need for extensive sampling in the traditional PRM algorithm. This innovative approach significantly reduces the number of required samples and decreases path planning time. The process begins with the collection of historical AIS data from the autonomous vessel’s navigation area, followed by comprehensive data-cleaning procedures to eliminate invalid and incomplete records. Subsequently, an enhanced DP compression algorithm is employed to streamline the cleaned AIS data, minimizing waypoint data while retaining essential trajectory characteristics. Intersection points among various vessel trajectories are then calculated, and these points, along with the compressed AIS data, form the foundational dataset for path searching. Building upon the traditional PRM framework, the proposed hybrid PRM algorithm integrates the B-spline algorithm to smooth and optimize the generated paths. Comparative experiments conducted against the standard PRM algorithm, A*, and Dijkstra algorithms demonstrate that the hybrid PRM approach not only reduces planning time but also achieves superior path smoothness. These improvements enhance both the efficiency and accuracy of path planning for maritime autonomous surface ships (MASS), marking a significant advancement in autonomous maritime navigation. Full article

Dual marine lifting systems are complicated, fully actuated mechatronics systems with multi-input and multi-output capabilities. The anti-swing cooperative lifting control of dual marine lifting systems with dual ships’ sway, heave, and roll motions is still open. The uncertainty regarding system parameters makes the task of achieving stable performance more challenging. To adjust both the attitude and position of large distributed-mass payloads to their target positions, this paper presents a time-delay-based sliding mode-tracking controller for cooperative dual marine lifting systems impacted by sea wave disturbances. Firstly, a dynamic model of a dual marine lifting system is established by using Lagrange’s method. Then, a kinematic coupling-based cooperative trajectory planning strategy is proposed by analyzing the coupling relationship between the dual marine lifting system and dual ship motion. After that, an improved sliding mode tracking controller is proposed by using time-delay estimation technology, which estimates unknown system parameters online. The finite-time convergence of full-state variables is rigorously proven. Finally, the simulation results verify the designed controller in terms of anti-swing control performance. The hardware experiments revealed that the proposed controller significantly reduces the actuator positioning errors by 83.33% compared with existing control methods. Full article

Inland waterways, characterized by their complex, narrow paths, see significantly higher traffic volumes compared to maritime routes, increasing the regulatory demands on traffic management. Predictive modeling of ship traffic flows, utilizing real AIS historical data, enhances route and docking planning for ships and port managers. This approach boosts transportation efficiency and safety in inland waterway navigation. Nevertheless, AIS data are flawed, marred by noise, disjointed paths, anomalies, and inconsistent timing between points. This study introduces a data processing technique to refine AIS data, encompassing segmentation, outlier elimination, missing point interpolation, and uniform interval resampling, aiming to enhance trajectory analysis reliability. Utilizing this refined data processing approach on ship trajectory data yields independent, complete motion profiles with uniform timing. Leveraging the Transformer model, denoted TRFM, this research integrates processed AIS data from the Yangtze River to create a predictive dataset, validating the efficacy of our prediction methodology. A comparative analysis with advanced models such as LSTM and its variants demonstrates TRFM’s superior accuracy, showcasing lower errors in multiple metrics. TRFM’s alignment with actual trajectories underscores its potential for enhancing navigational planning. This validation not only underscores the method’s precision in forecasting ship movements but also its utility in risk management and decision-making, contributing significantly to the advancement in maritime traffic safety and efficiency. Full article

The analysis of large amounts of vessel trajectory data can facilitate more complex traffic management and route planning, thereby reducing the risk of accidents. The application of deep learning methods in vessel trajectory prediction is becoming more and more widespread; however, due to the complexity of the marine environment, including the influence of geographical environmental factors, weather factors, and real-time traffic conditions, predicting trajectories in less constrained maritime areas is more challenging than in path network conditions. Ship trajectory prediction methods based on kinematic formulas work well in ideal conditions but struggle with real-world complexities. Machine learning methods avoid kinematic formulas but fail to fully leverage complex data due to their simple structure. Deep learning methods, which do not require preset formulas, still face challenges in achieving high-precision and long-term predictions, particularly with complex ship movements and heterogeneous data. This study introduces an innovative model based on the transformer structure to predict the trajectory of a vessel. First, by processing the raw AIS (Automatic Identification System) data, we provide the model with a more efficient input format and data that are both more representative and concise. Secondly, we combine convolutional layers with the transformer structure, using convolutional neural networks to extract local spatiotemporal features in sequences. The encoder and decoder structure of the traditional transformer structure is retained by us. The attention mechanism is used to extract the global spatiotemporal features of sequences. Finally, the model is trained and tested using publicly available AIS data. The prediction results on the field data show that the model can predict trajectories including straight lines and turns under the field data of complex terrain, and in terms of prediction accuracy, our model can reduce the mean squared error by at least $6\times {10}^{-4}$ compared with the baseline model. Full article

Overtaking situations are commonly encountered in maritime navigation, and the overtaking process involves various risk factors that significantly contribute to collision incidents. It is crucial to conduct research on the maneuvering behaviors and decision-making processes associated with ship overtaking. This paper proposes a method based on the analysis of ship maneuvering performance to investigate overtaking behaviors in navigational channels. A relative motion model is established for both the overtaking and the overtaken vessels, and the inter-vessel distance is calculated, taking into account the psychological perceptions of the ship’s driver. A decision-making model for ship overtaking is presented to provide a safety protocol for overtaking maneuvers. Applying this method to overtaking data from the South Channel shows that it effectively characterizes both the permissible overtaking space and the driver’s overtaking desire. Additionally, it enables the prediction of optimal overtaking timing and strategies based on short-term trajectory forecasts. Thus, this method not only offers a safe overtaking plan for vessels but also provides auxiliary information for decision making in intelligent ship navigation. Full article

This research focuses on integrating seakeeping indices into the trajectory planning of a mother ship in order to minimize risks during UAV (unmanned aerial vehicle) takeoff and landing in challenging sea conditions. By considering vessel dynamics and environmental factors, the proposed trajectory planning algorithm computes optimal paths that prioritize the stability and safety of the ship, mitigating the impact of adverse weather on UAV operations. Specifically, the new adaptive weather routing model presented is based on a genetic algorithm. The model uses the previously evaluated response amplitude operators (RAOs) for the reference ship at different velocities and encounter angles, along with weather forecast data provided by the global wave model (GWAM). Preliminary evaluations confirm the effectiveness of the presented model in significantly improving the reliability of autonomous UAV operations from a mother ship across all encountered sea state conditions, particularly when compared with a graph-based solution. The current results clearly demonstrate that it is possible to achieve appreciable improvements in ship seakeeping performance, thereby making UAV-related operations safer. Full article

This study addresses the challenges of maritime traffic management in the western waters of Taiwan, a region characterized by substantial commercial shipping activity and ongoing environmental development. Using 2023 Automatic Identification System (AIS) data, this study develops a robust feature extraction framework involving data cleaning, anomaly trajectory point detection, trajectory compression, and advanced processing techniques. Dynamic Time Warping (DTW) and the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithms are applied to cluster the trajectory data, revealing 16 distinct maritime traffic patterns, key navigation routes, and intersections. The findings provide fresh perspectives on analyzing maritime traffic, identifying high-risk areas, and informing safety and spatial planning. In practical applications, the results help navigators optimize route planning, improve resource allocation for maritime authorities, and inform the development of infrastructure and navigational aids. Furthermore, these outcomes are essential for detecting abnormal ship behavior, and they highlight the potential of route extraction in maritime surveillance. Full article

In addressing the high costs, inefficiencies, and limitations of purely digital simulations in maritime trials for unmanned vessel path planning, this paper introduces a ship virtual path planning simulation test system. This system, unbound by temporal and spatial constraints, vividly showcases the navigational performance of vessels. After analyzing the virtual testing requirements for the autonomous navigation performance of unmanned surface vehicles (USVs), we established the overall framework of this system. Data-driven by a numerical simulation platform, the system achieves synchronized operation between physical and virtual platforms and supports interactive path planning simulations between USVs and the virtual testing system. Furthermore, to address the limitations of traditional ship trajectory planning evaluation, this paper develops a global path planning fitness evaluation function that comprehensively considers trajectory safety, navigation distance, and vessel stability, achieving optimal comprehensive routes through the particle swarm optimization algorithm. Test results indicate an average roll reduction of 14.31% in the planned routes, with a slight increase in navigation distance. By integrating two-dimensional curve simulation with three-dimensional visualization, this paper not only overcomes the limitations of purely physical and purely virtual simulations but also enhances the overall credibility and intuitiveness of the simulation. Experimental results validate the system’s effectiveness, providing a novel method for autonomous navigation testing and evaluation of USVs. Full article

Ship path planning is the core problem of autonomous driving of smart ships and the basis for avoiding obstacles and other ships reasonably. To achieve this goal, this study improved the traditional A* algorithm to propose a new method for ship collision avoidance path planning by combining the multi-target point artificial potential field algorithm (MPAPF). The global planning path was smoothed and segmented into multi-target sequence points with the help of an improved A* algorithm and fewer turning nodes. The improved APF algorithm was used to plan the path of multi-target points locally, and the ship motion constraints were considered to generate a path that was more in line with the ship kinematics. In addition, this method also considered the collision avoidance situation when ships meet, carried out collision avoidance operations according to the International Regulations for Preventing Collisions at Sea (COLREGs), and introduced the collision risk index (CRI) to evaluate the collision risk and obtain a safe and reliable path. Through the simulation of a static environment and ship encounter, the experimental results show that the proposed method not only has good performance in a static environment but can also generate a safe path to avoid collision in more complex encounter scenarios. Full article