Publications

A Geometric Analysis-Based Safety Assessment Framework for MASS Route Decision-Making in Restricted Waters

Published in arXiv preprints [eess.SY], 2025

To enhance the safety of Maritime Autonomous Surface Ships (MASS) navigating in restricted waters, this paper aims to develop a geometric analysis-based route safety assessment (GARSA) framework, specifically designed for their route decision-making in irregularly shaped waterways. Utilizing line and point geometric elements to define waterway boundaries, the framework enables to construct a dynamic width characterization function to quantify spatial safety along intricate waterways. An iterative method is developed to calculate this function, enabling an abstracted spatial property representation of the waterways. Based on this, we introduce a navigational safety index that balances global navigational safety and local risk to determine the safest route. To accommodate ship kinematic constraints, path modifications are applied using a dynamic window approach. A case study in a simulated Port of Hamburg environment shows that GARSA effectively identifies safe routes and avoids the risk of entering narrow waterways in an autonomous manner, thereby prioritizing safety in route decision-making for MASS in confined waters.

Recommended citation: Xu, Z., Wang, Z., Li, H., Yu, D., Yang, Z., & Wang, J. (2025). A Geometric Analysis-Based Safety Assessment Framework for MASS Route Decision-Making in Restricted Waters. arXiv preprint arXiv:2501.06670.
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Target tracking control for Unmanned Surface Vehicles: An end-to-end deep reinforcement learning approach

Published in Ocean Engineering, 2024

Target tracking serves as a fundamental motion control function for Unmanned Surface Vehicles (USVs), requiring the USV to rapidly approach a moving target without prior knowledge of its behavior. However, system time lag, underactuation, and environmental disturbances often lead to response delays, degrading tracking efficiency. To address this, we propose a deep reinforcement learning-based end-to-end control method aimed at enhancing the USV’s tracking efficiency and responsiveness. Unlike conventional approaches, this method directly learns the mapping from sensor observations to control commands, optimizing decision-making and control actions within a unified framework. A specific deep reinforcement learning algorithm for target tracking is designed based on soft actor–critic framework and integrating predictive target information into the observation space to learn an anticipatory control policy. This paradigm enables the USV to comprehensively account for target movement uncertainty and its own maneuverability under environmental disturbances. Comparative studies are conducted using a high-fidelity simulator that considers the USV’s nonlinear dynamics and external influences. The results demonstrate that the proposed method outperforms conventional pure pursuit-based strategy, exhibiting a more efficient and adaptive tracking behavior, akin to human driving habits.

Recommended citation: Wang, Z., Hu, Q., Wang, C., Liu, Y., & Xie, W. (2025). Target tracking control for Unmanned Surface Vehicles: An end-to-end deep reinforcement learning approach. Ocean Engineering, 317, 120059.
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Hybrid Physics-ML Modeling for Marine Vehicle Maneuvering Motions in the Presence of Environmental Disturbances

Published in ArXiv Preprints, 2024

A hybrid physics-machine learning modeling framework is proposed for the surface vehicles' maneuvering motions to address the modeling capability and stability in the presence of environmental disturbances. From a deep learning perspective, the framework is based on a variant version of residual networks with additional feature extraction. Initially, an imperfect physical model is derived and identified to capture the fundamental hydrodynamic characteristics of marine vehicles. This model is then integrated with a feedforward network through a residual block. Additionally, feature extraction from trigonometric transformations is employed in the machine learning component to account for the periodic influence of currents and waves. The proposed method is evaluated using real navigational data from the 'JH7500' unmanned surface vehicle. The results demonstrate the robust generalizability and accurate long-term prediction capabilities of the nonlinear dynamic model in specific environmental conditions. This approach has the potential to be extended and applied to develop a comprehensive high-fidelity simulator.

Recommended citation: Wang, Z., Cheng, J., Xu, L., Hao, L., & Peng, Y. (2024). Hybrid Physics-ML Modeling for Marine Vehicle Maneuvering Motions in the Presence of Environmental Disturbances. arXiv preprint arXiv:2411.13908.
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Online prediction of ship maneuvering motions based on adaptive weighted ensemble learning under dynamic changes

Published in Engineering Applications of Computational Fluid Mechanics, 2024

Dynamic changes in ship maneuverability challenge the accuracy and effectiveness of ship maneuvering models. This paper proposes an online prediction method based on the adaptive weighted ensemble learning framework, which can adaptively update the model according to changes in maneuverability, especially for reoccurring changes. The method contains two main mechanisms: the change monitoring mechanism and the adaptive weighting mechanism. The former identifies the change in ship dynamics and decides when to incorporate a new base model; the latter adjusts the weights of the base models to align with current scenarios, thus ensuring the predictive accuracy. To assess the method’s effectiveness under varying ship dynamics, the online prediction of ship maneuvering motions under speed-induced dynamic changes is investigated. Compared with the offline model, the result demonstrates the superiority of the adaptive weighted ensemble model. The proposed method can consistently provide accurate predictions in the scenarios with reoccurring changes, and can also enhance the model capability by adjusting weights to cope with some unencountered changes.

Recommended citation: Yu, Y., Hao, H., Wang, Z., Peng, Y., & Xie, S. (2024). Online prediction of ship maneuvering motions based on adaptive weighted ensemble learning under dynamic changes. Engineering Applications of Computational Fluid Mechanics, 18(1), 2341922.
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Adaptive online modeling of ship maneuvering motion based on error monitoring - 基于误差监测机制的船舶操纵运动自适应在线建模

Published in Chinese journal of ship research - 中国舰船研究, 2024

Recommended citation: Yu, Y., Liu, S., Wang, Z., et al. Adaptive online modeling of ship maneuvering motion based on error monitoring[J]. Chinese Journal of Ship Research, 2025, 20(1): 1–7 (in Chinese). DOI: 10.19693/j.issn.1673-3185.04019

Data-Driven Modeling of Unmanned Surface Vehicle’s Maneuvering Motion Based on Real Navigational Data - 基于实航数据驱动的无人艇操纵运动辨识建模

Published in Ship Building of China - 中国造船, 2024

This study conducts a data-driven modeling research of the “Jinghai” unmanned surface vehicle based on lake trial data. The objective is to develop a kinetic model that captures the actual characteristics of the vehicle. The framework incorporates the nonlinear hydrodynamic model structure and the model of the propulsion system as prior knowledge constraints. Support vector regression is employed to complete the modeling process using a training dataset excited by a sequence of random rudder angles. The study places a particular emphasis on assessing the feasibility of modeling with environmental disturbances. The results demonstrate that the proposed method enables rapid construction of a nonlinear model for unmanned surface vehicles. The prior model structure effectively mitigates the impact of waves and currents in navigational data. The model accurately predicts the motion states during turning and zigzag trials, and exhibits good generalization ability.

Recommended citation: Wang, Z., Cheng, J., Xie, W., Song, R., Peng, Y.. Data-Driven Modeling of Unmanned Surface Vehicle's Maneuvering Motion Based on Real Navigational Data[J]. Ship Building of China, 2024, 65(1). doi: 10.3969/j.issn.1000-4882.2024.01.013

A data driven method for multi-step prediction of ship roll motion in high sea states

Published in Ocean Engineering, 2023

Ship roll motion in high sea states has large amplitudes and nonlinear dynamics, and its prediction is significant for operability, safety, and survivability. This paper presents a novel data-driven methodology to provide a multi-step prediction of ship roll motions in high sea states. A hybrid neural network is proposed that combines long short-term memory (LSTM) and convolutional neural network (CNN) in parallel. The motivation is to extract the nonlinear dynamic characteristics and the hydrodynamic memory information through the advantage of CNN and LSTM, respectively. For the feature selection, the time histories of motion states and wave heights are selected to involve sufficient information. Taken a scaled KCS as the study object, the ship motions in sea state 7 irregular long-crested waves are simulated and used for the validation. The results show that at least one period of roll motion can be accurately predicted. Compared with the single LSTM and CNN methods, the proposed method has better performance in predicting the amplitude of roll angles. Besides, the comparison results also demonstrate that selecting motion states and wave heights as feature space improves the prediction accuracy, verifying the effectiveness of the proposed method.

Recommended citation: Zhang, D., Zhou, X., Wang, Z. H., Peng, Y., & Xie, S. R. (2023). A data driven method for multi-step prediction of ship roll motion in high sea states. Ocean Engineering, 276, 114230.
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Black-box modeling of ship maneuvering motion based on multi-output nu-support vector regression with random excitation signal

Published in Ocean Engineering, 2022

This paper proposes a novel method for offline black-box modeling of ship maneuvering by utilizing the training data from random maneuvers under medium rudder angle with random amplitude and duration. The identification algorithm adopted is a multi-output ν(‘nu’)-Support Vector Regression, MO-ν-SVR, that has higher computational efficiency and better operability than a conventional ν-SVR. The ONRT vessel is taken as the study object, and numerical simulations are conducted to provide the training, validation and testing datasets. The superiority of the proposed random maneuver over the standard zig-zag maneuver is demonstrated by a contrastive study where the excitation signals from the random maneuver and the 20°/20° zig-zag maneuver are used for training the model separately. To examine the robustness of the proposed modeling method and the identified model, three levels of white noise are added into the raw simulation data for training the model. To explore the effectiveness and generalization ability of the identified model on different motion patterns of ship maneuvering, course-keeping, course-changing, and turning motions are examined separately. The results demonstrate that the model trained by the excitation signals of the random maneuver has better generalization ability and robustness, verifying the feasibility and practicality of the proposed modeling method.

Recommended citation: Zhang, Y. Y., Wang, Z. H., & Zou, Z. J. (2022). Black-box modeling of ship maneuvering motion based on multi-output nu-support vector regression with random excitation signal. Ocean Engineering, 257, 111279.
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Nonparametric modeling of ship maneuvering motion based on self-designed fully connected neural network

Published in Ocean Engineering, 2022

A self-designed fully connected neural network is proposed for nonparametric modeling of ship maneuvering motion by extracting the dynamic characteristics of ship motion from observed input-output data. The automatic design is conducted by Bayesian optimization, which aims at minimizing the validation error with respect to different hyperparameter settings of the neural network, including learning rate, activation function, as well as network depth and widths. Gaussian process is adopted as the surrogate model to estimate the unobserved validation errors, and lower confidence bound is adopted for trading off exploration and exploitation in the process of optimization. Taking the KVLCC2 ship as the study object, the experimental data provided by Hamburg Ship Model Basin is utilized for modeling and validation of prediction, and the detailed procedures to prepare and preprocess the input-output data are discussed. After well designed and trained, the selected neural network model is evaluated by the prediction results of ship maneuvers in comparison with the experimental data. It shows the high prediction accuracy and strong generalization ability of the established neural network model, indicating the effectiveness of the proposed modeling method.

Recommended citation: He, H. W., Wang, Z. H., Zou, Z. J., & Liu, Y. (2022). Nonparametric modeling of ship maneuvering motion based on self-designed fully connected neural network. Ocean Engineering, 251, 111113.
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Identification modeling and prediction of ship maneuvering motion based on LSTM deep neural network

Published in Journal of Marine Science and Technology, 2021

This paper proposes a novel system identification scheme to obtain a MIMO model of ship maneuvering motion, which can leverage the temporal correlation from the constructed training data to learn the underlying feasible model robust to extraneous noise. The scheme is based on long–short-term-memory (LSTM) deep neural network, which is more easily trained than traditional feedforward neural network with more complicated network structure. First, multiple datasets of simulated standard maneuvers (10°/10° and 20°/20° zigzag, 35° turning circle) of a KVLCC2 model are artificially polluted with white noise of various levels and used simultaneously to train the deep neural network model. Meanwhile, the data of 15°/15° zigzag maneuver are used to facilitate the training process to alleviate overfitting problem. Second, different datasets of modified zigzag tests are used to validate the generalization performance and robustness to noise of the trained neural network model. The training and validation results demonstrate that a mapping between the dynamics of ship motion and the computation in LSTM deep neural network is correctly identified. This mapping indicates that the complex nonlinear features of ship maneuvering motion can be learned from the measured temporal data, using standard training techniques for deep neural networks. An equivalent LSTM deep neural network model with better generalization performance and robustness is established, and its accuracy in predicting ship maneuvering motion is validated.

Recommended citation: Jiang, Y., Hou, X. R., Wang, X. G., Wang, Z. H., Yang, Z. L., & Zou, Z. J. (2022). Identification modeling and prediction of ship maneuvering motion based on LSTM deep neural network. Journal of Marine Science and Technology, 27(1), 125-137.
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Construction of Maritime Route Networks for USVs in Complex Waterways Based on Geometric Element Analysis

Published in IEEE 4th International Conference on Information Technology, Big Data and Artificial Intelligence, 2024

The construction of maritime route networks is crucial for the autonomous navigation of unmanned surface vehicles (USV), as it supports global coordination among multi-USV systems in narrow and interconnected waterways, thereby avoiding path conflicts and blockages. This paper proposes a method for constructing route networks based on geometric element analysis (RN-GEA). https://ieeexplore.ieee.org/abstract/document/10867893/

Recommended citation: Xu, Z., Wang, Z., & Xie, W. (2024, December). Construction of Maritime Route Networks for USVs in Complex Waterways Based on Geometric Element Analysis. In 2024 IEEE 4th International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA) (Vol. 4, pp. 1624-1628). IEEE.

Optimizing Roll Motion Forecasting for Unmanned Surface Vessels Using PSO-Enhanced LSTM Networks

Published in IEEE 4th International Conference on Information Technology, Big Data and Artificial Intelligence, 2024

https://ieeexplore.ieee.org/abstract/document/10867884/

Recommended citation: Qu, T., Wang, Z., & Xie, W. (2024, December). Optimizing Roll Motion Forecasting for Unmanned Surface Vessels Using PSO-Enhanced LSTM Networks. In 2024 IEEE 4th International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA) (Vol. 4, pp. 1539-1543). IEEE.

Evaluation of Parametric Modeling Method for Ship Maneuvering Motion With Experimental Data

Published in ASME 2023 42nd International Conference on Offshore Mechanics and Arctic Engineering (OMAE 2023), 2023

Recommended citation: Zhong, Y. X., Wang, Z. H., & Zou, Z. J. (2023, June). Evaluation of Parametric Modeling Method for Ship Maneuvering Motion With Experimental Data. In International Conference on Offshore Mechanics and Arctic Engineering (Vol. 86878, p. V005T06A046). American Society of Mechanical Engineers.

Online modeling of ship maneuvering motion with varying loading conditions

Published in ASME 2023 42nd International Conference on Offshore Mechanics and Arctic Engineering (OMAE 2023), 2023

Recommended citation: Yu, Y. H., Wang, Z. H., Qu, D., Song, R., & Peng, Y. (2023, June). Online modeling of ship maneuvering motion with varying loading conditions. In International Conference on Offshore Mechanics and Arctic Engineering (Vol. 86878, p. V005T06A047). American Society of Mechanical Engineers.