Advances in Marine Vehicles, Automation and Robotics—2nd Edition

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 2024 | Viewed by 4038

Special Issue Editors

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
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Guest Editor
Division of Mechanical Engineering, Korea Maritime and Ocean University, Busan 49112, Republic of Korea
Interests: robotics and control; artificial intelligence; humanoid robots; Unmanned Underwater Vehicles; robust control; ultra-high-speed control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Journal of Marine Science and Engineering is pleased to announce a Special Issue entitled "Advances in Marine Vehicles, Automation and Robotics—2nd Edition", which is based on the great success of our previous Special Issue with the same title.

Although we have made significant strides in exploring the surface of the Earth and outer space, we still have a limited understanding of the mysteries hidden in the depths of the ocean. The ocean is home to a vast array of natural and mineral resources, and with the depletion of resources on land, there is an increasing demand to explore the ocean's wealth. The ocean covers approximately 70% of the Earth's surface area, making it a valuable source of untapped resources. As the global population and resource consumption continue to grow, there is a growing need to focus on the ocean. Unfortunately, current techniques for undersea development are limited and challenging. As a result, there is a significant push to develop advanced oceanographic systems to explore and exploit the vast oceanic environment. With the advancement of oceanography research and marine resource exploration, marine mobile platforms have gained prominence, assuming a pivotal role in various marine missions including resource exploration, ocean observation, environmental monitoring, and sea area investigations. These platforms come in various types, such as Unmanned Surface Vehicles (USVs), Remotely Operated Vehicles (ROVs), Autonomous Underwater Vehicles (AUVs), Underwater Gliders (UGs), and marine animal robots, among others. Different from ground or aerial vehicles, marine vehicles operate in a marine environment, which is usually unknown, dynamic, hostile, and unconstructed. In such a complex and uncertain environment, it is difficult for marine vehicles to accomplish one or multiple missions and to realize full autonomy. In particular, the harshness of the underwater environment and the nature of signaling make navigation, guidance, and control challenging.

Following the success of the first edition of this Special Issue, "Advances in Marine Vehicles, Automation and Robotics", in 2023, this second Special Issue aims to showcase ongoing research in underwater vehicles and robotics, with a focus on practical applications and enhancing marine vehicle automation. Our goal is to create a platform that brings together diverse perspectives, including practitioners and leading researchers, to explore the potential of and address breakthroughs in marine vehicles and robotics. We invite high-quality original contributions, including technical papers addressing uncertainties in marine vehicle control and autonomy, reviews, surveys, theoretical works, and cutting-edge experimental approaches. We are particularly interested in novel techniques and advancements that shape the future of marine vehicles and robotics. Engineering and scientific articles related to marine vehicle instrumentation and data analysis, aiming to deepen our understanding of the marine environment, are encouraged. The scope encompasses modeling, control, navigation, cooperation, guidance, state estimation, and localization of marine vehicles and robotics. Submissions related to simulations, real-time sea trials, and testbed applications are highly valued in this Special Issue. All submitted papers will undergo a rigorous peer-review process by leading researchers worldwide, welcoming various types of articles, including research articles, review articles, technical notes, and short communications. We seek contributions spanning a broad range of topics related (but not limited) to the following:

  • Maritime Autonomous Surface Ships (MASSs), Autonomous Surface Vehicles (ASVs), Unmanned Surface Vehicles (USVs), Autonomous Underwater Vehicles (AUVs), Remotely Operated Vehicles (ROVs), Unmanned Underwater Vehicles (UUVs), Underwater Gliders, and Underwater Vehicle Manipulator Systems (UVMSs);
  • Deep-Sea Mining Technologies;
  • Swarms of unmanned vehicles, multiple vehicle mission control and planning, and cooperative surface and underwater vehicles;
  • Marine vehicle navigation, guidance, control, and path planning;
  • SLAM, localization, mapping, and tracking;
  • Sensor and actuator systems;
  • CFD applications;
  • Structural models and structural analysis;
  • Vehicle model tests, applications, case studies, field trials, and experimental results for propulsion systems;
  • Control, modeling, simulation, and optimization of propulsion systems;
  • Path following, path planning, trajectory planning, and automatic collision avoidance;
  • Applications of artificial intelligent (AI)/machine learning (ML) in autonomous marine networks;
  • Intelligent and adaptive control architectures;
  • Fault diagnosis and fault tolerance;
  • Automation systems and energy integration;
  • Docking and charging stations;
  • Propulsion systems and thruster allocation;
  • Software and hardware-in-the-loop simulation;
  • Architecture, concepts, methods, and technologies for underwater communications and networks;
  • Underwater Internet of the Things;
  • Maritime Internet of Things;
  • Modeling and digital twin modeling and control of vessels and unmanned marine vehicles;
  • Implementation of disruptive technologies in navigation (blockchain, IoT, and machine learning);
  • The use of AI for route planning, navigation, and optimization;
  • Vision-based 3D modeling for marine applications;
  • Marine renewable energy for robotic systems.

We look forward to receiving your valuable submissions.

Dr. Mai The Vu
Prof. Dr. Hyeung-Sik Choi
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

  • path planning and path following
  • system modeling and simulation
  • marine and underwater navigation
  • formation control
  • collision avoidance
  • robotics and control
  • cables and mooring
  • docking and berthing control
  • learning and AI
  • autopilot
  • motion control and optimization

Related Special Issue

Published Papers (5 papers)

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Research

21 pages, 5994 KiB  
Article
Performability Evaluation of Autonomous Underwater Vehicles Using Phased Fault Tree Analysis
by Sungil Byun and Dongik Lee
J. Mar. Sci. Eng. 2024, 12(4), 564; https://doi.org/10.3390/jmse12040564 - 27 Mar 2024
Viewed by 790
Abstract
This paper presents a phased fault tree analysis (phased-FTA)-based approach to evaluate the performability of Autonomous Underwater Vehicles (AUVs) in real time. AUVs carry out a wide range of missions, including surveying the marine environment, searching for specific targets, and topographic mapping. For [...] Read more.
This paper presents a phased fault tree analysis (phased-FTA)-based approach to evaluate the performability of Autonomous Underwater Vehicles (AUVs) in real time. AUVs carry out a wide range of missions, including surveying the marine environment, searching for specific targets, and topographic mapping. For evaluating the performability of an AUV, it is necessary to focus on the mission-dependent components and/or subsystems, because each mission exploits different combinations of devices and equipment. In this paper, we define a performability index that quantifies the ability of an AUV to perform the desired mission. The novelty of this work is that the performability of the AUV is evaluated based on the reliability and performance of the relevant resources for each mission. In this work, the component weight, expressing the degree of relevance to the mission, is determined using a ranking system. The proposed ranking system assesses the performance of the components required for each mission. The proposed method is demonstrated under various mission scenarios with different sets of faults and performance degradations. Full article
(This article belongs to the Special Issue Advances in Marine Vehicles, Automation and Robotics—2nd Edition)
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17 pages, 5023 KiB  
Article
Effect of Sampling Rate in Sea Trial Tests on the Estimation of Hydrodynamic Parameters for a Nonlinear Ship Manoeuvring Model
by Haitong Xu, P. Pires da Silva and C. Guedes Soares
J. Mar. Sci. Eng. 2024, 12(3), 407; https://doi.org/10.3390/jmse12030407 - 26 Feb 2024
Viewed by 787
Abstract
This paper explores the impact of sampling rates during sea trials on the estimation of hydrodynamic parameters in a nonlinear manoeuvring model. Sea trials were carried out using an offshore patrol vessel and test data were collected. A nonlinear manoeuvring model is introduced [...] Read more.
This paper explores the impact of sampling rates during sea trials on the estimation of hydrodynamic parameters in a nonlinear manoeuvring model. Sea trials were carried out using an offshore patrol vessel and test data were collected. A nonlinear manoeuvring model is introduced to characterise the ship’s manoeuvring motion, and the truncated least squares support vector machine is employed to estimate nondimensional hydrodynamic coefficients and their corresponding uncertainties using the 25°–25° zigzag test. To assess the influence of the sampling rates, the training set is resampled offline with 14 sampling rates, ranging from 0.2 Hz to 5 Hz, encompassing a rate 10 times the highest frequency component of the signal of interest. The results show that the higher sampling rate can significantly diminish the parameter uncertainty. To obtain a robust estimation of linear and nonlinear hydrodynamic coefficients, the sampling rate should be higher than 10 times the highest frequency component of the signal of interest, and 3–5 Hz is recommended for the case in this paper. The validation is also carried out, which indicates that the proposed truncated least square support vector machine can provide a robust parameter estimation. Full article
(This article belongs to the Special Issue Advances in Marine Vehicles, Automation and Robotics—2nd Edition)
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0 pages, 4822 KiB  
Article
Model Predictive Control of Counter-Rotating Motors for Underwater Vehicles Considering Unbalanced Load Variation
by Shukuan Zhang, Yunxiang Nan, Yusen Zhang, Chuan Xiang and Mai The Vu
J. Mar. Sci. Eng. 2024, 12(2), 330; https://doi.org/10.3390/jmse12020330 - 15 Feb 2024
Viewed by 522
Abstract
The propulsion system for underwater vehicles, driven by a counter-rotating permanent magnet synchronous motor (CRPMSM), can enhance the operational stability and efficiency of the vehicle. Due to the influence of complex underwater flows, the load imbalance of CRPMSM’s dual counter-rotating rotors may lead [...] Read more.
The propulsion system for underwater vehicles, driven by a counter-rotating permanent magnet synchronous motor (CRPMSM), can enhance the operational stability and efficiency of the vehicle. Due to the influence of complex underwater flows, the load imbalance of CRPMSM’s dual counter-rotating rotors may lead to severe issues of dual-rotor desynchronization rotation. Combining traditional vector control (VC) with master-slave control strategies can address the desynchronization problem when CRPMSM’s load changes. However, it results in significant speed fluctuations and a long transition time during the transition from load disturbance to synchronous rotation. This paper introduces a model predictive control (MPC) strategy to effectively resolve this issue. The incremental MPC model is established based on the mathematical model of CRPMSM in the dq coordinate system. The predictive control system forecasts the d- and q-axis components of stator currents for the next four control cycles. It selects the optimal control increments to minimize the cost function based on current predictions and different inverter voltage states. The obtained optimal d- and q-axis components of stator voltage are used to control CRPMSM under unbalanced load disturbances. Simulation results demonstrate that, compared to the VC strategy, CRPMSM utilizing the MPC strategy exhibits better dynamic performance with faster speed response and reduced torque fluctuations during load and speed variations. Full article
(This article belongs to the Special Issue Advances in Marine Vehicles, Automation and Robotics—2nd Edition)
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18 pages, 1396 KiB  
Article
A Rigid-Flexible Coupling Dynamic Model for Robotic Manta with Flexible Pectoral Fins
by Yilin Qu, Xiao Xie, Shucheng Zhang, Cheng Xing, Yong Cao, Yonghui Cao, Guang Pan and Baowei Song
J. Mar. Sci. Eng. 2024, 12(2), 292; https://doi.org/10.3390/jmse12020292 - 06 Feb 2024
Viewed by 712
Abstract
The manta ray, exemplifying an agile swimming mode identified as the median and paired fin (MPF) mode, inspired the development of underwater robots. Robotic manta typically comprises a central rigid body and flexible pectoral fins. Flexible fins provide excellent maneuverability. However, due to [...] Read more.
The manta ray, exemplifying an agile swimming mode identified as the median and paired fin (MPF) mode, inspired the development of underwater robots. Robotic manta typically comprises a central rigid body and flexible pectoral fins. Flexible fins provide excellent maneuverability. However, due to the complexity of material mechanics and hydrodynamics, its dynamics are rarely studied, which is crucial for the advanced control of robotic manta (such as trajectory tracking, obstacle avoidance, etc.). In this paper, we develop a multibody dynamic model for our novel manta robot by introducing a pseudo-rigid body (PRB) model to consider passive deformation in the spanwise direction of the pectoral fins while avoiding intricate modeling. In addressing the rigid-flexible coupling dynamics between flexible fins and the actuation mechanism, we employ a sequential coupling technique commonly used in fluid-structure interaction (FSI) problems. Numerical examples are provided to validate the MPF mode and demonstrate the effectiveness of the dynamic model. We show that our model performs well in the rigid-flexible coupling analysis of the manta robot. In addition to the straight-swimming scenario, we elucidate the viability of tailoring turning gaits through systematic variations in input parameters. Moreover, compared with finite element and CFD methods, the PRB method has high computational efficiency in rigid-flexible coupling problems. Its potential for real-time computation opens up possibilities for future model-based control. Full article
(This article belongs to the Special Issue Advances in Marine Vehicles, Automation and Robotics—2nd Edition)
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20 pages, 6667 KiB  
Article
Kinematic Calibration for the 3-UPS/S Shipborne Stabilized Platform Based on Transfer Learning
by Min Xu, Wenjie Tian and Xiangpeng Zhang
J. Mar. Sci. Eng. 2024, 12(2), 275; https://doi.org/10.3390/jmse12020275 - 02 Feb 2024
Cited by 1 | Viewed by 680
Abstract
The three-degrees-of-freedom (3-DOF) parallel robot is commonly employed as a shipborne stabilized platform for real-time compensation of ship disturbances. Pose accuracy is one of its most critical performance indicators. Currently, neural networks have been applied to the kinematic calibration of stabilized platforms to [...] Read more.
The three-degrees-of-freedom (3-DOF) parallel robot is commonly employed as a shipborne stabilized platform for real-time compensation of ship disturbances. Pose accuracy is one of its most critical performance indicators. Currently, neural networks have been applied to the kinematic calibration of stabilized platforms to compensate for pose errors and enhance motion accuracy. However, collecting a large amount of measured configuration data for robots entails high costs and time, which restricts the widespread use of neural networks. In this study, a “transfer network” is established by combining fine-tuning with a Back Propagation (BP) neural network. This network takes the motion transmission characteristics inherent in the ideal kinematic model as prior knowledge and transfers them to a network trained based on the actual poses. Compared with the conventional BP neural network trained by actual poses alone, the transfer network shows significant performance advantages, effectively solving the problems of low prediction accuracy and weak generalization ability in the case of small-sample measured data. Considering this, the impact pattern of the sample number of the actual pose on the effectiveness of transfer learning is revealed through the construction of multiple transfer network models under varying sample numbers of the actual pose, providing valuable marine engineering guidance. Finally, simulated sea-service experiments were conducted on the 3-UPS/S shipborne stabilized platform to validate the correctness and superiority of the proposed method. Full article
(This article belongs to the Special Issue Advances in Marine Vehicles, Automation and Robotics—2nd Edition)
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