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Applied Sciences
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Modeling, Guidance and Control of Marine Robotics
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Modeling, Guidance and Control of Marine Robotics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Marine Science and Engineering".

Deadline for manuscript submissions: 20 November 2024 | Viewed by 4250

Special Issue Editor

Dr. Zaopeng Dong

E-Mail Website
Guest Editor
Associate Professor, School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430062, China
Interests: control systems engineering; ocean engineering; autonomous underwater vehicles; marine robots; intelligent ship

Special Issue Information

Dear Colleagues,

It is our pleasure to invite you to contribute to this Special Issue entitled “Modeling, Guidance and Control of Marine Robotics”.

Marine robotics includes a wide range of devices, from autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs) to gliders and unmanned surface vehicles (USVs). These devices can be used for a range of tasks, such as mapping the seafloor, collecting data on ocean currents and water quality, and monitoring marine life. Modeling, guidance and control are critical aspects of marine robotics that enable robots and autonomous vehicles to perform their intended tasks effectively and efficiently. In recent years, the modeling, guidance and control of marine robotics have attracted worldwide attention.

Modeling involves the development of mathematical models that describe the behavior and performance of marine robots. These models can be used to simulate a robot's performance in various scenarios, such as in different sea states or with different payloads. Modeling is essential for designing and optimizing marine robots and for predicting their behavior in different operating conditions. Guidance refers to the process of providing a robot with instructions or commands to follow. In marine robotics, guidance can involve determining the robot's position and orientation, calculating the optimal path for the robot to follow, and adjusting the robot's trajectory to avoid obstacles or other hazards. Guidance systems often rely on various sensors, such as sonar, GPS, and cameras, to provide information about the robot's surroundings and position. Control means using algorithms and feedback systems to adjust the robot's behavior and ensure that it follows the desired trajectory or path. Control systems can vary depending on the specific application and the type of robot being used. For example, control systems for an AUV might involve adjusting its buoyancy to control its depth, while control systems for an ROV might involve adjusting the thrusters to maintain position and orientation.

Overall, modeling, guidance and control are essential for the effective operation of marine robots and autonomous vehicles. These technologies enable marine robots to navigate complex environments, avoid obstacles, and perform tasks with precision and accuracy, ultimately leading to more efficient and effective marine exploration and research.

This Special Issue aims to address the recent advances in the modeling, guidance and control of marine robotics. Submissions can address, but are not limited to, the following topics:

  • Modeling of marine robotics;
  • Maneuverability modeling and analysis of marine robotics;
  • Seakeeping analysis and modeling of marine robotics;
  • Ship performance design and modeling analysis of marine robotics;
  • Guidance of marine robotics;
  • Video processing for intelligent marine robots;
  • Sensing technology for marine robotics;
  • Precision instrumentation for marine robots;
  • Integrated behavior and decision in marine robotics;
  • Control and operation of marine robotics;
  • Multi-robot communication and coordination;
  • Control of networked marine robots;
  • Evolutionary learning for swarm marine robotics;
  • Development and application of special marine robots.

Submissions of both original research articles and review articles are welcome. In addition, articles with remarkable contributions to recent conferences in this field are also welcomed to expand for publication in this Special Issue. We hope that this collection of articles will highlight the recent progress made in the area of marine robotics and serve as an inspiration for those working in this area.

Dr. Zaopeng Dong
Guest Editor

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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • marine robotics
  • ship design
  • autonomous underwater vehicles (AUVs)
  • unmanned surface vehicles (USVs)

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Published Papers (6 papers)

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Research

12 pages, 4069 KiB  
Article
A Method for Extracting Dynamic Vortex Acoustic Signal Characteristics in Island and Reef Channels Based on Time-Reversal Mirrors
by Min Yu, Hang Liu, Wei Zhou and Dingfan Fan
Appl. Sci. 2024, 14(16), 7042; https://doi.org/10.3390/app14167042 - 11 Aug 2024
Viewed by 333
Abstract
Ships navigating in channels with vortex fields face increased risks. However, these vortex fields can be monitored using acoustic methods. The key is to extract the phase characteristics of sound signals passing through the vortices. Using time-reversal mirrors, this paper studied the extraction [...] Read more.
Ships navigating in channels with vortex fields face increased risks. However, these vortex fields can be monitored using acoustic methods. The key is to extract the phase characteristics of sound signals passing through the vortices. Using time-reversal mirrors, this paper studied the extraction method of characteristics both numerically and experimentally, aiming to verify the effectiveness of the numerical simulation method. Starting from this point, the impact of different movement forms and scale changes in vortex fields on the acoustic signal extraction method was further investigated. The results indicate that with the iterations of time reversal (N < 6), the method is effective for uniformly moving vortex fields, when the vortex center moving speed Vw < 2.2 × 10−3 m/s and the radius diffusion speed Vr < 2.5 × 10−3 m/s. On the other hand, for oscillating vortex fields, it is effective when the oscillation amplitude LD < 0.15 m and the radius diffusion speed Vr < 2.4 × 10−3 m/s; meanwhile, the dynamic characteristics of the vortex field can be ignored by the phase extraction method based on time-reversal mirrors. Full article
(This article belongs to the Special Issue Modeling, Guidance and Control of Marine Robotics)
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21 pages, 4002 KiB  
Article
A Novel Method of Time-Varying Formation Control Based on a Directed Graph for Multiple Autonomous Underwater Vehicles
by Gang Shao, Lei Wan and Huixi Xu
Appl. Sci. 2024, 14(14), 6377; https://doi.org/10.3390/app14146377 - 22 Jul 2024
Viewed by 427
Abstract
Currently, autonomous underwater vehicles (AUVs) are facing various challenges, rendering multiple-AUV (multi-AUV) formation control a pivotal research direction. The issues surrounding formation control for a multi-AUV system to establish time-varying formations must be investigated. This paper discusses the formation protocol of multi-AUV systems [...] Read more.
Currently, autonomous underwater vehicles (AUVs) are facing various challenges, rendering multiple-AUV (multi-AUV) formation control a pivotal research direction. The issues surrounding formation control for a multi-AUV system to establish time-varying formations must be investigated. This paper discusses the formation protocol of multi-AUV systems in order to establish the defined time-varying formations. First, when these systems establish formations, the speed of each AUV can be equivalent. After that, consensus-based methods are used to solve the time-varying formation-control problem. The necessary and sufficient process of multi-AUV in achieving time-varying formations is proved. Furthermore, the formula for the time-varying formation center function is provided. Further, we present a protocol law for multi-AUVs to establish time-varying formations. Finally, the theoretical results of a simulation are presented, which validate the formation protocol. Full article
(This article belongs to the Special Issue Modeling, Guidance and Control of Marine Robotics)
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22 pages, 6161 KiB  
Article
Virtual Streamline Traction: Formation Cooperative Obstacle Avoidance Based on Dynamical Systems
by Yiping Liu, Jianqiang Zhang, Yuanyuan Zhang and Jiarui Wang
Appl. Sci. 2024, 14(14), 6087; https://doi.org/10.3390/app14146087 - 12 Jul 2024
Viewed by 386
Abstract
Formation obstacle avoidance is a critical aspect of cooperation among unmanned surface vehicles (USVs). In practical scenarios involving multiple USVs, managing obstacle avoidance during formation assembly and navigation is essential to ensure the success of cooperative tasks. This study devised a formation cooperative [...] Read more.
Formation obstacle avoidance is a critical aspect of cooperation among unmanned surface vehicles (USVs). In practical scenarios involving multiple USVs, managing obstacle avoidance during formation assembly and navigation is essential to ensure the success of cooperative tasks. This study devised a formation cooperative obstacle-avoidance scheme utilizing dynamical systems (DS). The traditional interfered fluid dynamical system (IFDS) applied in two-dimensional planes was enhanced to address local minima issues. Furthermore, robust virtual structure patterns were implemented to effectively decouple velocity vectors. Streamlines were optimized by adjusting velocity amplitudes within specific distance intervals, facilitating precise formation assembly amidst multiple obstacles. Additionally, a novel inter-vehicle disturbance method, distinct from the IFDS, was developed to enhance inter-vehicle collision avoidance. The effectiveness of the proposed method in enabling USV formations to adeptly navigate obstacles while maintaining formation integrity and collision-avoidance capabilities was analyzed theoretically and confirmed through simulation. Full article
(This article belongs to the Special Issue Modeling, Guidance and Control of Marine Robotics)
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19 pages, 5771 KiB  
Article
Analysis of Electromagnetic Field Characteristics of Wave Glider
by Taotao Xie, Jiawei Zhang, Dawei Xiao and Qing Ji
Appl. Sci. 2024, 14(11), 4800; https://doi.org/10.3390/app14114800 - 1 Jun 2024
Viewed by 385
Abstract
A wave glider is an ocean observation platform that utilizes wave energy to drive and solar energy to power. Its metal structure will generate related electromagnetic fields due to corrosion and underwater motion. In the detection of weak electromagnetic field signals underwater, its [...] Read more.
A wave glider is an ocean observation platform that utilizes wave energy to drive and solar energy to power. Its metal structure will generate related electromagnetic fields due to corrosion and underwater motion. In the detection of weak electromagnetic field signals underwater, its own electromagnetic field characteristics will have an impact on signal detection. To study the applicability of electric field sensors and magnetic field sensors on wave glider platforms, the structural characteristics of the wave glider were analyzed, and the installation positions of electric field sensors and magnetic field sensors were designed based on the different motion states of the water surface mother body and underwater towing body. The measured electromagnetic field data of the wave glider platform were measured, and the measured data were analyzed. It was determined that the interference electric field energy under typical working conditions of the wave glider was mainly concentrated within 1 Hz, which decreased with increasing frequency, and the magnitude was mV/m. The magnitude of the interference magnetic field is several tens of nT, indicating that the electromagnetic field interference is significant during the working state of the wave glider. Installing an electric field sensor directly at the bottom of the wave glider will cause significant noise interference, while installing the magnetic field sensor directly at the bottom of the tractor will affect the servo and the shaking-induced magnetic field. Moreover, wave gliders should not use electric field signals below 1 Hz as signal sources, but they can utilize axial frequency electromagnetic fields to detect weak electromagnetic signals underwater. Full article
(This article belongs to the Special Issue Modeling, Guidance and Control of Marine Robotics)
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17 pages, 34672 KiB  
Article
Multi-AUV Control Method Based on Inverse Optimal Control of Integrated Obstacle Avoidance Algorithm
by Gang Shao, Lei Wan and Huixi Xu
Appl. Sci. 2023, 13(22), 12198; https://doi.org/10.3390/app132212198 - 10 Nov 2023
Cited by 1 | Viewed by 732
Abstract
Under complex underwater conditions, multiple AUVs work in one area and they need to cooperate for complicated missions. In this study, a design method was applied for multiple autonomous underwater vehicles (AUVs) that are distributed in an area and suddenly receive a command. [...] Read more.
Under complex underwater conditions, multiple AUVs work in one area and they need to cooperate for complicated missions. In this study, a design method was applied for multiple autonomous underwater vehicles (AUVs) that are distributed in an area and suddenly receive a command. Using this method, the AUVs work according to their own state and reach the target while avoiding obstacles automatically in the process of collection. A new optimal control method is proposed that achieves the consensus of multiple AUVs as well as offering obstacle avoidance capability with minimal control effort. A non-quadratic obstacle avoidance cost function was constructed from the perspective of inverse optimal control. The distributed analytic optimal control law depends only on the local information that can be generated by the communication topology, which guarantees the proposed behavior, so that the control law does not require information from all AUVs. A simulation and an experiment were performed to verify the consensus and obstacle avoidance effect. Full article
(This article belongs to the Special Issue Modeling, Guidance and Control of Marine Robotics)
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21 pages, 7460 KiB  
Article
Collaborative Search and Target Capture of AUV Formations in Obstacle Environments
by Xinyu Hu, Yu Shi, Guiqiang Bai and Yanli Chen
Appl. Sci. 2023, 13(15), 9016; https://doi.org/10.3390/app13159016 - 7 Aug 2023
Cited by 4 | Viewed by 1220
Abstract
When performing cooperative search operations underwater, multi-autonomous underwater vehicles formations may encounter array-type obstacles such as gullies and bumps. To safely traverse the obstacle domain, this paper balances convergence time, transformation distance and sensor network power consumption, and proposes a Formation Comprehensive Cost [...] Read more.
When performing cooperative search operations underwater, multi-autonomous underwater vehicles formations may encounter array-type obstacles such as gullies and bumps. To safely traverse the obstacle domain, this paper balances convergence time, transformation distance and sensor network power consumption, and proposes a Formation Comprehensive Cost (FCC) model to achieve collision avoidance of the formations. The FCC model is used instead of the fitness function of the genetic algorithm to solve the assignment of capture positions and the improved neural self-organizing map (INSOM) algorithm is proposed to achieve efficient path-planning during the capture process. The simulation experiments in 3D space verify that the proposed scheme can improve the efficiency of robot deployment while ensuring safety. Full article
(This article belongs to the Special Issue Modeling, Guidance and Control of Marine Robotics)
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