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Advanced Research in Sustainable and Intelligent Navigation Control Systems for...
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Advanced Research in Sustainable and Intelligent Navigation Control Systems for Marine Vehicles

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Ocean Engineering".

Deadline for manuscript submissions: 15 August 2025 | Viewed by 5801

Special Issue Editors

Dr. Silvia Donnarumma

E-Mail Website
Guest Editor
Department of Electrical, Electronic, Telecommunications Engineering and Naval Architecture, University of Genova, Genoa, Italy
Interests: control optimization; dynamic positioning system; vessel motion control; marine system identification; marine system simulation; unconventional marine propellers; control synthesis with actuator saturations
Special Issues, Collections and Topics in MDPI journals
Dr. Mai The Vu

E-Mail Website
Guest Editor
Department of Unmanned Vehicle Engineering, Sejong University, Seoul 05000, Republic of Korea
Interests: system dynamics; mechatronics; underwater vehicles; automation and robotics; trajectory tracking; path planning; multi-body dynamic modeling, intelligent navigation, autonomous vehicles
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, autonomous vehicles have been increasingly used in a wide array of applications due to their great adaptability and the availability of several systems capable of accomplishing complex targets such as collision avoidance, track-keeping, target tracking, auto-berthing, and path planning. However, these systems have not yet been adequately integrated into the maritime field. It is hypothesized that the introduction of smart technologies onboard and the possible adoption of new ship designs for these vehicles will have a potential positive impact on the energy efficiency process, thereby further improving safety and relieving humans of repetitive tasks that can lead to high accounts of error. Unfortunately, the feasibility of integrating such large-scale systems into the main bridge control interface as decision support or command advice, and the advantages associated with energy preservation, is not yet clear.

Therefore, this Special Issue of the Journal of Marine Science and Engineering (JMSE) titled ‘Development of Sustainable and Intelligent Navigation Control Systems for Marine Vehicles’ invites submissions of original research articles and reviews that underscore the novelties in the methodologies of developing integrated and collaborative navigation systems, highlight the impact of these systems on energy consumption and energy efficiency, as well as state the associated challenges.

Potential topics of interest include, but are not limited to, the following:

  • Ship, autonomous surface vehicles (ASVs), unmanned surface vehicles (USVs), autonomous underwater vehicles (AUVs), remotely operated vehicles (ROVs), unmanned underwater vehicles (UUVs), and underwater gliders;
  • Swarms of unmanned vehicles, multiple vehicle mission control and planning, and cooperative surface and underwater vehicles;
  • Guidance, navigation and path planning;
  • Kinematics and vehicle dynamics;
  • Sensor and actuator systems;
  • Power system operation;
  • Vehicle model tests, applications, case studies, field trials, and experimental results for propulsion system;
  • Control, modeling, simulation and optimization of propulsion system;
  • Path following, path planning, trajectory planning, and automatic collision avoidance;
  • Applications of artificial intelligent (AI)/machine learning (ML) in autonomous marine networks;
  • Machine learning methods for marine robotics;
  • Intelligent and adaptive control architectures;
  • Cooperative control and navigation in multi-unmanned systems;
  • Fault diagnosis and fault tolerance;
  • Automation system and energy integration;
  • Docking and charging stations;
  • Propulsion system;
  • Thruster allocation;
  • Software and hardware-in-the-loop simulation.

We look forward to receiving your valuable submissions.

Dr. Silvia Donnarumma
Dr. Mai The Vu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • vessel motion control
  • path and trajectory planning
  • cooperative path planning
  • marine autonomous surface vessel (MASS)
  • autonomous underwater vehicle
  • control optimization
  • autonomous ship
  • autonomous
  • control allocation
  • cooperative motion control
  • integrated systems
  • support decision system
  • energy efficient
  • sustainability

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Published Papers (7 papers)

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Research

22 pages, 1537 KiB  
Article
Time Domain Design of a Marine Target Tracking System Accounting for Environmental Disturbances
by Camilla Fruzzetti, Cristiana Buzzurro, Silvia Donnarumma and Michele Martelli
J. Mar. Sci. Eng. 2024, 12(11), 2058; https://doi.org/10.3390/jmse12112058 - 13 Nov 2024
Viewed by 389
Abstract
Environmental disturbances represent significant challenges to the performance and accuracy of autonomous systems, especially in marine environments, where their impact varies based on disturbance severity and the employed guidance law. This paper comprehensively investigates a marine target tracking system using time-domain simulations incorporating [...] Read more.
Environmental disturbances represent significant challenges to the performance and accuracy of autonomous systems, especially in marine environments, where their impact varies based on disturbance severity and the employed guidance law. This paper comprehensively investigates a marine target tracking system using time-domain simulations incorporating realistic environmental disturbances. Three guidance laws and four key performance indicators are analysed to evaluate system performance under disturbed and ideal conditions. A robust and systematic evaluation pipeline is developed and applied to a case study featuring a scaled tugboat model. This approach provides a reliable method to assess tracking accuracy and robustness in adverse conditions. The results are selected from a wide range of possibilities to show the effect of the disturbances on the selected target tracking motion control scenario with two manoeuvres and two environmental conditions. The results are measured through the selected key performance indicators, and several phases are identified for each manoeuvre to extend the analysis not only to the global KPI values but also to the partial values of defined phases. They reveal the quantitative effects of environmental disturbances, exposing different system behaviours and trends. These findings demonstrate the effectiveness of the proposed pipeline in quantifying tracking system performance, delivering useful understandings of the system under environmental disturbances. The broader implications of this study are substantial, offering enhanced predictive accuracy for the performance of the analysed systems, particularly in the context of target tracking. Furthermore, introducing numerical key performance indicators facilitates a more rigorous comparison of different system characteristics, enabling informed decisions in designing and optimising autonomous operations in challenging environments. Full article
(This article belongs to the Special Issue Advanced Research in Sustainable and Intelligent Navigation Control Systems for Marine Vehicles)
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17 pages, 6692 KiB  
Article
Adaptive Pitch-Tracking Control with Dynamic and Static Gains for Remotely Operated Towed Vehicles
by Cong Tian, Hang Xu, Songkai Ren, Longchuan Guo, Xiaoqing Tian and Jiyong Wang
J. Mar. Sci. Eng. 2024, 12(11), 1953; https://doi.org/10.3390/jmse12111953 - 31 Oct 2024
Viewed by 505
Abstract
The pitch angle regulation in Remotely Operated Towed Vehicles (ROTVs) is essential to ensure the robustness of emitted signals within the maritime surveillance domain. Characterized by inherent nonlinear dynamics and stochastic uncertainties, the pitch angle model poses significant challenges to conventional tracking controls [...] Read more.
The pitch angle regulation in Remotely Operated Towed Vehicles (ROTVs) is essential to ensure the robustness of emitted signals within the maritime surveillance domain. Characterized by inherent nonlinear dynamics and stochastic uncertainties, the pitch angle model poses significant challenges to conventional tracking controls relying on linearization. This study introduces an adaptive pitch-control algorithm designed for ROTVs, which adeptly manages nonlinear dynamics as well as unmeasurable states through a synergistic integration of dynamic and static gains. A key feature of our approach is the incorporation of a high-order observer that adeptly estimates the system’s unmeasurable states, thereby enhancing control precision. Our proposed algorithm greatly exceeds traditional PID and fuzzy PID methods in both settling time and steady-state error, particularly in high-order nonlinear and unmeasurable state scenarios. Compared to sliding mode control, the proposed control strategy improved the settling time by 74% and the steady-state error was enhanced from 10−6 to 10−8, as confirmed by numerical simulations. The efficacy of the algorithm in achieving the desired tracking trajectories highlights its potential for deep-water operations and fine-tuned attitude adjustments for ROTVs. Full article
(This article belongs to the Special Issue Advanced Research in Sustainable and Intelligent Navigation Control Systems for Marine Vehicles)
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27 pages, 10956 KiB  
Article
Distributed Neuroadaptive Formation Control for Aerial Base Station-Assisted Hovercraft Systems with Mixed Disturbances
by Peiyun Ye, Renhai Yu and Qihe Shan
J. Mar. Sci. Eng. 2024, 12(11), 1946; https://doi.org/10.3390/jmse12111946 - 31 Oct 2024
Viewed by 440
Abstract
Effectively addressing the formation control of ABS-assisted hovercraft systems with heterogeneities, unavailable leaders’ convex combination states, nonlinearities, and mixed disturbances poses significant challenges. This paper proposes a distributed neuroadaptive formation tracking strategy of ABS-assisted hovercraft systems for the first time, where aerial base [...] Read more.
Effectively addressing the formation control of ABS-assisted hovercraft systems with heterogeneities, unavailable leaders’ convex combination states, nonlinearities, and mixed disturbances poses significant challenges. This paper proposes a distributed neuroadaptive formation tracking strategy of ABS-assisted hovercraft systems for the first time, where aerial base stations (ABSs) are composed of unmanned aerial vehicles (UAVs) for data distribution and computation offloading. Firstly, UAVs are designed to track the virtual-leader while shaping a fixed formation, and the observer is devised for each follower hovercraft to estimate the convex combination states of UAVs. Then, output regulation equations are employed to transform heterogeneous systems into a compact form via the Kronecker product, while neural networks (NNs) are introduced to compensate for model nonlinearities. Furthermore, based on random differential equations (RDEs) combined with Lyapunov theory, the noise-to-state practical stability in probability (NSPS-P) property of the error dynamics under mixed disturbances can be obtained. Finally, simulation examples demonstrate that the outputs of follower hovercrafts rapidly achieve a time-varying formation and rotate around convex combination states of leader UAVs simultaneously. Full article
(This article belongs to the Special Issue Advanced Research in Sustainable and Intelligent Navigation Control Systems for Marine Vehicles)
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18 pages, 7240 KiB  
Article
Artificial Neural Network-Based Route Optimization of a Wind-Assisted Ship
by Cem Guzelbulut, Timoteo Badalotti, Yasuaki Fujita, Tomohiro Sugimoto and Katsuyuki Suzuki
J. Mar. Sci. Eng. 2024, 12(9), 1645; https://doi.org/10.3390/jmse12091645 - 14 Sep 2024
Cited by 1 | Viewed by 813
Abstract
The International Maritime Organization aims for net-zero carbon emissions in the maritime industry by 2050. Among various alternatives, route optimization holds an important place as it does not require any additional component-related costs. Especially for wind-assisted ships, the effectiveness of different sailing systems [...] Read more.
The International Maritime Organization aims for net-zero carbon emissions in the maritime industry by 2050. Among various alternatives, route optimization holds an important place as it does not require any additional component-related costs. Especially for wind-assisted ships, the effectiveness of different sailing systems can be improved significantly through route optimization. However, finding the ship’s optimal route is computationally expensive when the totality of possible weather conditions is taken into consideration. To determine the optimal route that minimizes energy consumption, an energy model based on the environmental conditions, ship route and ship speed was built using artificial neural networks. The energy consumed for given input data was calculated using a ship dynamics model and a database was generated to train the artificial neural networks, which predict how much energy is consumed depending on the route followed in given environmental conditions. Then, such networks were exploited to derive the optimal routes for all the relevant operational conditions. It was found that route optimization can reduce the overall ship energy consumption depending on the weather conditions of the environment by up to 9.7% without any increase in voyage time and by up to 35% with a 10% delay in voyage time. The proposed methodology can be applied to any ship by training real weather conditions and provides a framework for reducing energy consumption and greenhouse gas emissions during the service life of ships. Full article
(This article belongs to the Special Issue Advanced Research in Sustainable and Intelligent Navigation Control Systems for Marine Vehicles)
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18 pages, 641 KiB  
Article
Distributed Estimator-Based Containment Control for Multi-AUV Systems Subject to Input Saturation and Unknown Disturbance
by Liangang Yin, Zheping Yan and Jian Xu
J. Mar. Sci. Eng. 2024, 12(7), 1200; https://doi.org/10.3390/jmse12071200 - 17 Jul 2024
Viewed by 601
Abstract
This article addresses the containment control issue for multi-AUV systems with the intervention of both external disturbance and input saturation. Firstly, a distributed estimator is established for the sake of acquiring precise estimation information of the desired position and its derivative for each [...] Read more.
This article addresses the containment control issue for multi-AUV systems with the intervention of both external disturbance and input saturation. Firstly, a distributed estimator is established for the sake of acquiring precise estimation information of the desired position and its derivative for each follower AUV in the system. Next, on the basis of the proposed distributed estimator, a virtual control law is designed for each follower AUV. Then, due to the difficulty in obtaining accurate information about the derivative of the virtual control law, a linear tracking differentiator is introduced. Additionally, a disturbance observer is employed to tackle the composite disturbance, which mainly contains the internal model uncertainties and external bounded disturbances. Meanwhile, the issue of input saturation is handled by constructing the auxiliary system. Furthermore, a containment control law is designed with the assistance of the introduced linear tracking differentiator, the established disturbance observer, and the constructed auxiliary system. Additionally, the Lyapunov stability theory is applied to analyze the stability of the multi-AUV system. Finally, simulation results are given to confirm the feasibility of the proposed containment control scheme. Full article
(This article belongs to the Special Issue Advanced Research in Sustainable and Intelligent Navigation Control Systems for Marine Vehicles)
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21 pages, 12567 KiB  
Article
Research and Application of Panoramic Visual Perception-Assisted Navigation Technology for Ships
by Chiming Wang, Xiaocong Cai, Yanan Li, Runxuan Zhai, Rongjiong Wu, Shunzhi Zhu, Liangqing Guan, Zhiqiang Luo, Shengchao Zhang and Jianfeng Zhang
J. Mar. Sci. Eng. 2024, 12(7), 1042; https://doi.org/10.3390/jmse12071042 - 21 Jun 2024
Cited by 2 | Viewed by 923
Abstract
In response to challenges such as narrow visibility for ship navigators, limited field of view from a single camera, and complex maritime environments, this study proposes panoramic visual perception-assisted navigation technology. The approach includes introducing a region-of-interest search method based on SSIM and [...] Read more.
In response to challenges such as narrow visibility for ship navigators, limited field of view from a single camera, and complex maritime environments, this study proposes panoramic visual perception-assisted navigation technology. The approach includes introducing a region-of-interest search method based on SSIM and an elliptical weighted fusion method, culminating in the development of the ship panoramic visual stitching algorithm SSIM-EW. Additionally, the YOLOv8s model is improved by increasing the size of the detection head, introducing GhostNet, and replacing the regression loss function with the WIoU loss function, and a perception model yolov8-SGW for sea target detection is proposed. The experimental results demonstrate that the SSIM-EW algorithm achieves the highest PSNR indicator of 25.736, which can effectively reduce the stitching traces and significantly improve the stitching quality of panoramic images. Compared to the baseline model, the YOLOv8-SGW model shows improvements in the P, R, and mAP50 of 1.5%, 4.3%, and 2.3%, respectively, its mAP50 is significantly higher than that of other target detection models, and the detection ability of small targets at sea has been significantly improved. Implementing these algorithms in tugboat operations at ports enhances the fields of view of navigators, allowing for the identification of targets missed by AISs and radar systems, thus ensuring operational safety and advancing the level of vessel intelligence. Full article
(This article belongs to the Special Issue Advanced Research in Sustainable and Intelligent Navigation Control Systems for Marine Vehicles)
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20 pages, 3462 KiB  
Article
Analytical Solution of Time-Optimal Trajectory for Heaving Dynamics of Hybrid Underwater Gliders
by Mai The Vu, Seong Han Kim, Van P. Nguyen, Nguyen Xuan-Mung, Jiafeng Huang, Dong-Wook Jung and Hyeung-Sik Choi
J. Mar. Sci. Eng. 2023, 11(12), 2216; https://doi.org/10.3390/jmse11122216 - 22 Nov 2023
Viewed by 1185
Abstract
Underwater vehicles have capacity limits for control inputs, within which their time-optimal trajectories (TOTs) can be formulated. In this study, the fastest trajectory for the depth control of a hybrid underwater glider (HUG) was found using buoyancy engines and propellers individually, and the [...] Read more.
Underwater vehicles have capacity limits for control inputs, within which their time-optimal trajectories (TOTs) can be formulated. In this study, the fastest trajectory for the depth control of a hybrid underwater glider (HUG) was found using buoyancy engines and propellers individually, and the decoupled heave dynamics of the HUG were defined using quadratic hydrodynamic damping. Because buoyancy engines always run at slow speeds, the buoyancy force was formulated based on the constant force rate of the engine. It was assumed that the nominal value of the heave dynamics parameters could be estimated; therefore, the analytical solution of heave dynamics could be formulated using the thrusting saturation and constant buoyancy force rate. Then, the shortest trajectory for depth control of the HUG could be established while considering the actuator saturation. To verify the effectiveness of the TOT in HUG heave dynamics, extensive tracking control simulations following the TOT were conducted. It was found that the proposed TOT helps the HUG reach the desired depth in the shortest arrival time, and its robust depth control showed good tracking performance in the presence of external bounded disturbances. Full article
(This article belongs to the Special Issue Advanced Research in Sustainable and Intelligent Navigation Control Systems for Marine Vehicles)
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