Browsing by Author "Zhang, Jinfen"

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COLREGs-Adaptive trajectory planning and decision-making in maritime autonomous surface ships
(2024-11-15) Han, Zhepeng; Wu, Da; Zhang, Jinfen; Huang, Tao; Han, Qing Long; Zhang, Mingyang
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Decision-making for collision avoidance and trajectory planning are critical technologies for maritime autonomous surface ships. These systems must align with regulatory frameworks such as the International Regulations for Preventing Collisions at Sea (COLREGs) and account for the ship's maneuvering capabilities for effective control and tracking. This study introduces a novel framework integrating regulatory consideration into the path-searching process, enhancing collision avoidance in both COLREG-compliant and non-compliant scenarios. The framework recasts the trajectory-planning problem into an optimal control problem and employs virtual obstacles and spatial–temporal navigation corridors consistent with collision-free decisions as constraints for trajectory optimization, improving navigation efficiency and comfort. The framework is validated through various encounter scenarios, and the results demonstrate that the proposed framework can produce superior collision avoidance decisions, while planning shorter navigation time and smoother collision-free trajectories, significantly improving the collision avoidance capability of maritime autonomous surface ships.
A deep learning method for the prediction of 6-DoF ship motions in real conditions
(2023-11) Zhang, Mingyang; Taimuri, Ghalib; Zhang, Jinfen; Hirdaris, Spyros
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This paper presents a deep learning method for the prediction of ship motions in 6 Degrees of Freedom (DoF). Big data streams of Automatic Identification System (AIS), now-cast, and bathymetry records are used to extract motion trajectories and idealise environmental conditions. A rapid Fluid-Structure Interaction (FSI) model is used to generate ship motions that account for the influence of surrounding water and ship-controlling devices. A transformer neural network that accounts for the influence of operational conditions on ship dynamics is validated by learning the data streams corresponding to ship voyages and hydro-meteorological conditions between two ports in the Gulf of Finland. Predictions for a ship turning circle and motion dynamics between these two ports show that the proposed method can capture the influence of operational conditions on seakeeping and manoeuvring.
A Dynamic Bayesian Network model to evaluate the availability of machinery systems in Maritime Autonomous Surface Ships
(2024-01) Han, Zhepeng; Zhang, Di; Fan, Liang; Zhang, Jinfen; Zhang, Mingyang
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
With their complex structure, multiple failure modes and lack of maintenance crew, the safety problem of Maritime Autonomous Surface Ships’ (MASS) machinery systems are becoming an important research topic. The present study presents an availability model for ship machinery systems incorporating a maintenance strategy based on Dynamic Bayesian Networks (DBN). First, the availability of conventional ship machinery systems is evaluated and used as a benchmark based on the configuration and planned maintenance strategy. Secondly, the availability of MASS machinery systems is compared to the benchmark, before the introduction of any changes to the ship's configuration and planned maintenance strategy. Finally, the availability improvement strategies, including redundant designs and planned maintenance strategies at port, are proposed based on sensitivity analysis and planned maintenance cost minimization. To exemplify the model's application, a case study of a cooling water system is explored. Based on a sensitivity analysis using the model, it is possible to decide which components need to be redundant. Different redundancy designs and corresponding planned maintenance strategies can be adopted to meet the availability demand. It is also shown that redundancy and enhanced detection capabilities reduce much of the planned maintenance cost. This framework can be used in the early design stages to determine whether the MASS machinery systems’ availability is at least equivalent to that of conventional ships, and has certain reference significance for redundant configuration designs and MASS planned maintenance strategy schedule.
A game-based decision-making method for multi-ship collaborative collision avoidance reflecting risk attitudes in open waters
(2024-12-01) Liu, Jiongjiong; Zhang, Jinfen; Yang, Zaili; Zhang, Mingyang; Tian, Wuliu
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
To accurately reflect risk attitudes towards ship intentions in multi-ship encounters, this paper develops a novel two-stage collaborative collision avoidance decision-making (CADM) model by incorporating intention prediction and real-time decision-making. We acquire prior knowledge of risk attitudes by analyzing Automatic Identification System (AIS) data and further estimate the probability distributions of encountering ship's risk attitude using Bayesian reasoning. By treating collision avoidance procedure as a static game with incomplete information, a predictive model for collision avoidance intentions is developed by taking account into risk attitude probabilities. Real-time decisions are then implemented according to different stages, and a collaborative CADM model is established by a game-decision cycle. Finally, a multi-ship encounter scenario is simulated under all combinations of risk attitudes, and the results are compared with those obtained under complete information. The results demonstrate that the proposed model can formulate avoidance actions that meet safety requirements under all combinations of risk attitudes. Further comparison with complete information proves the effectiveness of the risk attitude probability model, which is conducive to improving the decision-making flexibility and reducing complexity. The research findings enhance the collaborative decision-making, contributing to the development of autonomous navigation in open waters.
A machine learning method for the prediction of ship motion trajectories in real operational conditions
(2023-09-01) Zhang, Mingyang; Kujala, Pentti; Musharraf, Mashrura; Zhang, Jinfen; Hirdaris, Spyros
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This paper presents a big data analytics method for the proactive mitigation of grounding risk. The model encompasses the dynamics of ship motion trajectories while accounting for kinematic uncertainties in real operational conditions. The approach combines K-means and DB-SCAN (Density-Based Spatial Clustering of Applications with Noise) big data clustering methods with Principal Component Analysis (PCA) to group environmental factors. A Multiple-Output Gaussian Process Regression (MOGPR) method is consequently used to predict selected ship motion dynamics. Ship sway is defined as the deviation between a ship and her motion trajectory centreline. Surge accelerations are used to idealise the time-varying manoeuvring of ships in various routes. Operational conditions are simulated by Automatic Identification System (AIS), General Bathymetric Chart of the Oceans (GEBCO), and nowcast hydro-meteorological data records. A Dynamic Time Warping (DTW) method is adopted to identify ship centre-line trajectories along selected paths. The machine learning algorithm is applied for ship motion predictions of Ro-Pax ships operating between two ports in the Gulf of Finland. Ship motion dynamics are visualised along the ship’s route using a Gaussian Progress Regression (GPR) flow method. Results indicate that the present methodology may assist with predicting the probabilistic distribution of ship dynamics (speed, sway distance, drift angle, and surge accelerations) and grounding risk along selected ship routes.
A novel collaborative collision avoidance decision-making methodology based on potential collision areas for intelligent navigation
(2025-02-15) Liu, Jiongjiong; Zhang, Jinfen; Zhang, Mingyang; Xin, Xuri; Yang, Zaili
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Ship navigation safety is a crucial component of intelligent navigation, and collaborative collision avoidance plays a vital role in ensuring safety and conflict resolution by enhancing decision-making efficiency in multi-ship encounters. In this paper, a collaborative collision avoidance model is proposed to connect encounter risk and decision-making in a more intuitive way. It pioneers the quantification of collision risk based on potential collision areas and differentiates encountered ships, and a collaborative mechanism is further constructed to formulate a collision avoidance decision-making model. International Regulations for Preventing Collisions at Sea (COLREGs) are comprehensively considered by differentiating risk relationships among ships and specify their roles and responsibilities. A ship collaboration mechanism under action intention games is constructed based on rational thinking to form a decision-making model with game-decision cycles. The results demonstrate that the model can meet the safety requirements in case studies, providing a rational reflection, and accurately determines encounter stages. The results also indicate that ship differentiation and role assignment can adapt to abnormal actions. This research makes significant contributions to ship decision support in term of better collaboration among ships while reducing action conflicts, promoting the development of intelligent navigation technologies.
A novel data-driven method of ship collision risk evolution evaluation during real encounter situations
(2024-09) Liu, Jiongjiong; Zhang, Jinfen; Yang, Zaili; Wan, Chengpeng; Zhang, Mingyang
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Aiming at realizing collision risk quantitative evaluation among encounter ships, a novel data-driven evolution model is proposed concerning encounter evolution in maritime transportation. A probabilistic velocity obstacle with an elliptic conflict region is constructed by taking into account uncertainty. The degree of and time to domain violation are introduced to quantify collision risk levels under varying velocities. Then, a ship collision risk evolution model is formulated by considering spatial attributes, macro-level and micro-level evolution based on a realistic collision avoidance decision. The model parameters and their weights are determined by statistical analysis of historical encounter scenarios and the characteristics of encounter stages. Therefore, the model encapsulates the statistical characteristics of actual data, which improves its practical values. The results of case studies indicate that the collision risk evolution model can properly reflect collision risk, so that collision evolution stages can be classified accordingly for rational anti-collision guidance. It brings new contributions to risk visualization, collision avoidance decision-making, and collision accident analysis and responsibility determination.
A novel real-time collision risk awareness method based on velocity obstacle considering uncertainties in ship dynamics
(2021-01-15) Yuan, Xiaoli; Zhang, Di; Zhang, Jinfen; Zhang, Mingyang; Guedes Soares, C.
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
COLREGs-based collision risk awareness model is urgently needed in real-time operating conditions. However, this is a complicated problem under various encounter situations, some of which are very complex. In order to quantify the collision risk in real operating conditions, a novel risk-informed collision risk awareness approach is proposed for real-time operating conditions. Firstly, the ship's actions are identified based on the Automatic Identification System (AIS) data. Secondly, the uncertainty of ship action patterns is analyzed by regarding the target ships as velocity obstacles. Then, the collision risk model is utilized to assess the collision risk level based on the uncertainty in the non-linear velocity obstacles algorithm considering responsibility. Finally, some case studies are carried out based on the proposed model. In the model, the dynamic and uncertainty features of the ship action dynamics in real operating conditions are considered, which could benefit on reducing ship collision accidents and improving the development of technologies on intelligent collision avoidance decision makings.
Systems driven intelligent decision support methods for ship collision and grounding prevention : Present status, possible solutions, and challenges
(2025-01) Zhang, Mingyang; Taimuri, Ghalib; Zhang, Jinfen; Zhang, Di; Yan, Xinping; Kujala, Pentti; Hirdaris, Spyros
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Despite advancements in science and technology, ship collisions and groundings remain the most prevalent types of maritime accidents. Recent developments in accident prevention and mitigation methods have been bolstered by the rise of autonomous shipping, digital technologies, and Artificial Intelligence (AI). This paper provides an exhaustive review of the characteristics of fleets at risk over the past two decades, emphasizing the societal impacts of preventing collisions and groundings. It also delves into the key components of decision support systems from a ship's perspective and undertakes a systematic literature review on the foundations and applications of systems-driven decision support methods for ship collision and grounding prevention. The study covers risk analysis, damage evaluation, and ship motion prediction methods from 2002 to 2023. The conclusions indicate that modern ship science methods are increasingly valuable in ship design and maritime operations. Emerging multi-physics systems and AI-enabled predictive analytics show potential for future integration into intelligent decision support systems. The strategic research challenges include (1) underestimating the impacts of real operational conditions on ship safety, (2) the inherent limitations of static risk analysis and finite numerical methods, and (3) the need for rapid, probabilistic assessments of damage extents. The demands and trends suggest that leveraging big data analytics and rapid prediction methods, underpinned by digitalization and AI technologies, represents the most feasible way forward.
A three-dimensional ant colony algorithm for multi-objective ice routing of a ship in the Arctic area
(2022-12-15) Zhang, Chi; Zhang, Di; Zhang, Mingyang; Zhang, Jinfen; Mao, Wengang
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
The increasing shipping activities in the Arctic area pose challenges to a ship's safety and fuel saving in ice-covered waters. The optimal ship route planning can reduce the fuel consumption and navigation risk for ice-going ships in the Northeast Route. In this paper, a multi-objective ice routing model has been developed for searching optimal routes with two objectives, i.e., minimization of the fuel consumption and the total risk along a voyage, considering the time-varying ice data. Navigation risk is considered by applying a risk assessment model for Arctic navigation. A 3D-ACA (Three-Dimensional Ant Colony Algorithm) has been proposed and implemented in the developed ice routing model to make decisions on a ship's passing waypoints and sailing speeds along each waypoint. Finally, several case studies with different route planning objectives have been conducted to demonstrate the performance of the proposed model.
Use of Hybrid Causal Logic Method for Preliminary Hazard Analysis of Maritime Autonomous Surface Ships
(2022-06) Zhang, Di; Han, Zhepeng; Zhang, Kai; Zhang, Jinfen; Zhang, Mingyang; Zhang, Fan
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Recently, the safety issue of maritime autonomous surface ships (MASS) has become a hot topic. Preliminary hazard analysis of MASS can assist autonomous ship design and ensure safe and reliable operation. However, since MASS technology is still at its early stage, there are not enough data for comprehensive hazard analysis. Hence, this paper attempts to combine conventional ship data and MASS experiments to conduct a preliminary hazard analysis for autonomy level III MASS using the hybrid causal logic (HCL) method. Firstly, the hazardous scenario of autonomy level III MASS is developed using the event sequence diagram (ESD). Furthermore, the fault tree (FT) method is utilized to analyze mechanical events in ESD. The events involving human factors and related to MASS in the ESD are analyzed using Bayesian Belief Network (BBN). Finally, the accident probability of autonomy level III MASS is calculated in practice through historical data and a test ship with both an autonomous and a remote navigation mode in Wuhan and Nanjing, China. Moreover, the key influence factors are found, and the accident-causing event chains are identified, thus providing a reference for MASS design and safety assessment process. This process is applied to the preliminary hazard analysis of the test ship.