Recent Advances in Underwater Vehicles

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

Deadline for manuscript submissions: 31 July 2024 | Viewed by 1567

Special Issue Editor


E-Mail Website
Guest Editor
Department of Robot Engineering, Keimyung University, Dalseo-gu, Republic of Korea
Interests: underwater walking robot; underwater robotics; underwater vehicle

Special Issue Information

Dear Colleagues,

In the era of the Fourth Industrial Revolution, robot technology has become an essential technology. Especially in the maritime field, it is a crucial technology due to the unique environmental constraints posed by water, making it increasingly necessary for ocean exploration and the development of marine resources. Submarine robots have made significant advancements over the past 60 years. Starting in the 1950s to 1970s in the defense sector and transitioning to the private sector, particularly in the offshore industry, during the 1980s and 1990s. Subsequently, they began to be employed in various fields, such as marine survey, exploration, rescue missions, and patrolling, expanding their operational range to deeper and more distant ocean areas. This progress has been made possible by the collaboration of various fields of technology, including design, sensing, manufacturing, communication, and navigation technologies. This Special Issue aims to introduce the recent advances in underwater vehicles. The issue welcomes all kinds of underwater vehicles, such as ROV, AUV, UG, and UAUV, and all the research and review topics associated with underwater vehicles, such as novel design, navigation and control, planning, and decisions.

In this Special Issue, original research articles and reviews are all welcome, on recent theoretical and experimental works. Topics of interest for publication include, but are not limited to, the following:

  • Underwater robot
  • Unmanned underwater vehicles (ROV, AUV, etc.);
  • Underwater sensing, multi-modal sensor fusion, and manipulation for UUVs;
  • Vehicle guidance, navigation, path planning in UUVs;
  • Control and modeling for UUVs;
  • Cooperative underwater vehicle manipulator systems;
  • Networked UUVs;
  • Intelligence and autonomy for underwater robotic vehicles;
  • Machine Learning methods for underwater vehicles;
  • Unmanned aerial and underwater vehicle;
  • Ocean robotics;
  • Underwater detection;
  • Underwater robot vision;
  • CFD for underwater robots;

Dr. Seongyeol Yoo
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

  • underwater robots
  • underwater sensors
  • navigation
  • machine learning for underwater robots

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

22 pages, 9567 KiB  
Article
Position Tracking Control of 4-DOF Underwater Robot Leg Using Deep Learning
by Jin-Hyeok Bae and Jung-Yup Kim
Appl. Sci. 2024, 14(3), 1031; https://0-doi-org.brum.beds.ac.uk/10.3390/app14031031 - 25 Jan 2024
Viewed by 741
Abstract
This paper presents a novel hybrid control method for position tracking of an underwater quadruped walking robot. The proposed approach combines an existing position-tracking control method with a deep-learning neural network. The neural network compensates for non-linear dynamic characteristics, such as the effect [...] Read more.
This paper presents a novel hybrid control method for position tracking of an underwater quadruped walking robot. The proposed approach combines an existing position-tracking control method with a deep-learning neural network. The neural network compensates for non-linear dynamic characteristics, such as the effect of fluid, without relying on mathematical modeling. To achieve this, a Multi-Layer Perceptron neural network is designed to analyze joint torque in relation to the joint angle and angular velocity of the robot, as well as the position and orientation of the foot tip and environmental data. The improvement in tracking control performance is evaluated using a 4-DOF underwater robot leg. For the neural network design, position tracking control data, including dynamic characteristics, were collected through position command-based position tracking control. Afterward, a learning model was constructed and trained to predict joint torque related to the robot’s motion and posture. This learning process incorporates non-linear dynamic characteristics, such as joint friction and the influence of fluid, in the joint torque prediction. The proposed method is then combined with conventional task-space PD control to perform position-tracking control with enhanced performance. Finally, the proposed method is evaluated using the underwater robot leg and compared to a single task-space PD controller. The proposed method demonstrates higher position accuracy with similar joint torque output, thereby increasing compliance and tracking performance simultaneously. Full article
(This article belongs to the Special Issue Recent Advances in Underwater Vehicles)
Show Figures

Figure 1

19 pages, 8873 KiB  
Article
Establishment of a Pressure Variation Model for the State Estimation of an Underwater Vehicle
by Ji-Hye Kim, Thi Loan Mai, Aeri Cho, Namug Heo, Hyeon Kyu Yoon, Jin-Yeong Park and Sung-Hoon Byun
Appl. Sci. 2024, 14(3), 970; https://0-doi-org.brum.beds.ac.uk/10.3390/app14030970 - 23 Jan 2024
Viewed by 517
Abstract
This study presents a pressure variation model (PVM) derived from the regression analysis of dynamic pressure computed through numerical analysis to estimate the velocity of underwater vehicles. Furthermore, the drift angle estimation algorithm was developed using predicted velocities from PVM and pressure sensor [...] Read more.
This study presents a pressure variation model (PVM) derived from the regression analysis of dynamic pressure computed through numerical analysis to estimate the velocity of underwater vehicles. Furthermore, the drift angle estimation algorithm was developed using predicted velocities from PVM and pressure sensor differences. This approach estimates the single-motion states of underwater vehicles, such as straight, turning, and gliding. Furthermore, it confirms the viability of state estimation even in multiple motions involving turning and gliding motion with a drift angle and spiral motion. The comparison with numerical analysis results validated prediction accuracy within 15%. Full article
(This article belongs to the Special Issue Recent Advances in Underwater Vehicles)
Show Figures

Figure 1

Back to TopTop