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Electronics
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Underwater Robotics: Theory, Methods and Applications
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Underwater Robotics: Theory, Methods and Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: 15 March 2025 | Viewed by 2976

Special Issue Editors

Dr. Xiang Cao

E-Mail Website
Guest Editor
School of Artificial Intelligence, Anhui University, Hefei 230039, China
Interests: target searching and path planning of underwater vehicles
Dr. Yunhu Zhou

E-Mail Website
Guest Editor
School of Artificial Intelligence, Anhui University, Hefei 230039, China
Interests: robotics motion control
Dr. Juqi Hu

E-Mail Website
Guest Editor
School of Artificial Intelligence, Anhui University, Hefei 230039, China
Interests: Intelligent autonomous unmanned system

Special Issue Information

Dear Colleagues,

With the continuous deepening of ocean exploration, humans will face more and more extreme underwater operations that cannot be completed. Underwater robots have emerged as important tools for understanding and developing the ocean due to their unmanned, intelligent, and clustered capabilities. They have broad application prospects in fields such as oil and mineral exploration, geomorphic surveying, scientific research observation, aquaculture, pier dam inspection, ship cleaning, diving entertainment, underground pipeline inspection, military and national defense, etc. In order to better explore the latest breakthrough and innovative research achievements, current challenges, and corresponding solutions of underwater robots, Electronics plan to launch a Special Issue entitled "Underwater Robotics: Theory, Methods and Applications". Electronics welcomes contributions from experts, scholars, and researchers from relevant research fields.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

Topic

(1) Design and modeling of a new type of underwater robot

(2) Heterogeneous multi-sensor system perception of the underwater environment

(3) Image processing and recognition of the underwater environment

(4) Acoustic and optical image fusion in the underwater environment

(5) Communication networking technology for surface/underwater environment

(6) Navigation positioning and trajectory planning for underwater environments

(7) Formation and obstacle avoidance control of underwater robots

(8) Control of underwater robots based on deep reinforcement learning

(9) Underwater robot grasping and search technology

(10) Collaborative positioning and control of underwater robots

Dr. Xiang Cao
Dr. Yunhu Zhou
Dr. Juqi Hu
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. Electronics 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

  • design and modeling
  • perception
  • image processing and recognition
  • control
  • communication networking
  • navigation positioning
  • planning

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Published Papers (2 papers)

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Research

15 pages, 2347 KiB  
Article
An Underwater Multi-Label Classification Algorithm Based on a Bilayer Graph Convolution Learning Network with Constrained Codec
by Yun Li, Su Wang, Jiawei Mo and Xin Wei
Electronics 2024, 13(16), 3134; https://doi.org/10.3390/electronics13163134 - 7 Aug 2024
Viewed by 1106
Abstract
Within the domain of multi-label classification for micro-videos, utilizing terrestrial datasets as a foundation, researchers have embarked on profound endeavors yielding extraordinary accomplishments. The research into multi-label classification based on underwater micro-video datasets is still in the preliminary stage. There are some challenges: [...] Read more.
Within the domain of multi-label classification for micro-videos, utilizing terrestrial datasets as a foundation, researchers have embarked on profound endeavors yielding extraordinary accomplishments. The research into multi-label classification based on underwater micro-video datasets is still in the preliminary stage. There are some challenges: the severe color distortion and visual blurring in underwater visual imaging due to water molecular scattering and absorption, the difficulty in acquiring underwater short video datasets, the sparsity of underwater short video modality features, and the formidable task of achieving high-precision underwater multi-label classification. To address these issues, a bilayer graph convolution learning network based on constrained codec (BGCLN) is established in this paper. Specifically, modality-common representation is constructed to complete the representation of common information and specific information based on the constrained codec network. Then, the attention-driven double-layer graph convolutional network module is designed to mine the correlation information between labels and enhance the modality representation. Finally, the combined modality representation fusion and multi-label classification module are used to obtain the category classifier prediction. In the underwater video multi-label classification dataset (UVMCD), the effectiveness and high classification accuracy of the proposed BGCLN have been proved by numerous experiments. Full article
(This article belongs to the Special Issue Underwater Robotics: Theory, Methods and Applications)
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24 pages, 6730 KiB  
Article
USV Search Mission Planning Methodology for Lost Target Rescue on Sea
by Han Zhang, Yanyan Huang, Hucheng Qin and Ze Geng
Electronics 2023, 12(22), 4584; https://doi.org/10.3390/electronics12224584 - 9 Nov 2023
Cited by 3 | Viewed by 1478
Abstract
Quick and efficient mission planning is essential in maritime search and rescue (SAR). This includes defining the search area and developing an effective strategy. The task is fraught with challenges due to the difficulty of determining location information and the impact of complex [...] Read more.
Quick and efficient mission planning is essential in maritime search and rescue (SAR). This includes defining the search area and developing an effective strategy. The task is fraught with challenges due to the difficulty of determining location information and the impact of complex meteorological environments. The primary objective of SAR mission planning is the rapid deployment of unmanned surface vehicles (USVs) to the incident area. While many planning algorithms prioritize the shortest route, there’s a lack of mission planning measures that maximize SAR effectiveness. In addition, the joint deployment of USVs increases the success rate compared to individual operations. Therefore, this paper presents a task assignment framework for USVs in SAR missions that considers the probability of success and time constraints. USVs are used to search for lost targets, and the framework consists of the following three modules: (1) a module for predicting the location of the overboard target to be rescued; (2) a module for modeling the probability of mission success; (3) a module for assigning search tasks to USVs. The framework first analyzes the search area. Then, it predicts the target location with a stochastic particle method, which incorporates marine environment forecast data to update the mission target location. To improve the scientific nature of USV search and rescue mission plans, an evaluation model is developed to assess mission capability. Simulation experiments and task scheme analysis validate its effectiveness. Full article
(This article belongs to the Special Issue Underwater Robotics: Theory, Methods and Applications)
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