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Advances on Autonomous Underwater Vehicles (AUV)

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 8764

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Special Issue Editors

Prof. Dr. Bruno Miguel Ferreira

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Guest Editor

Technology and Science, Institute for Systems and Computer Engineering, 4200-465 Porto, Portugal
Interests: marine robotics; autonomous underwater vehicle (AUV); autonomous surface vehicles (ASV); guidance; control; coordination; localization; estimation; sensing

Dr. Nuno A. Cruz

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Guest Editor

Technology and Science, Institute for Systems and Computer Engineering, 4200-465 Porto, Portugal
Interests: marine robotics; adaptive sampling; control; guidance; autonomous underwater vehicles (AUV); autonomous surface vehicles (ASV); underwater system design
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Special Issue Information

Dear Colleagues,

Autonomous underwater vehicles (AUVs) have become the most effective technology for gathering ocean data while overcoming operational barriers, reducing costs, and precluding risks for humans. They are routinely employed to understand ocean processes occurring at multiple scales. Although AUVs have mostly been operating in the open sea, there has been an increasing extension of operations to rivers and lakes and to complex confined areas. The applications in which they have been employed have shaped their overall characteristics. In all these cases, AUV operations are benefiting from progresses in the state of the art in complementary research topics, from innovative mechanical solutions to advanced navigation and control, complex real-time image processing, and other machine learning techniques, just to name a few.

This Special Issue is dedicated to recent advances in AUV technology. The topics of interest are as follows:

Innovative AUV design, including mechanical and electrical components;
Localization, guidance, and control;
Mission management and planning;
Real-time data processing.

These research topics have been supporting new capabilities and new mission paradigms, whose applications are also suitable for this Special Issue, including (but not limited to):

Autonomous inspection and intervention;
Adaptive sampling;
Mapping and surveying;
Cooperative missions of autonomous vehicles.

Demonstrations of advances, supported by experimental data from field trials, and focused studies on autonomy, endurance, and efficiency are also welcome.

Prof. Dr. Bruno Miguel Ferreira
Prof. Dr. Nuno A. Cruz
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. Remote Sensing 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 2700 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

autonomous underwater vehicles
system design
autonomy
endurance
intelligence
adaptive mission
control
estimation
localization
field trials

Published Papers (4 papers)

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Research

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19 pages, 6623 KiB

Open AccessArticle

A Bio-Inspired MEMS Wake Detector for AUV Tracking and Coordinated Formation

by Qingyu Qiao, Xiangzheng Kong, Shufeng Wu, Guochang Liu, Guojun Zhang, Hua Yang, Wendong Zhang, Yuhua Yang, Licheng Jia, Changde He, Jiangong Cui and Renxin Wang

Remote Sens. 2023, 15(11), 2949; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15112949 - 05 Jun 2023

Cited by 1 | Viewed by 1180

Abstract

AUV (Autonomous Underwater Vehicle) coordinated formation can expand the detection range, improve detection efficiency, and complete complex tasks, which requires each AUV to have the ability to track and locate. A wake detector provides a new technical approach for AUV cooperative formation warfare. Now, most of the existing artificial lateral line detectors are for one-dimensional flow field applications, which are difficult to use for wake detection of AUVs. Therefore, based on the pressure gradient sensing mechanism of the canal neuromasts, we apply Micro-Electro-Mechanical System (MEMS) technology to develop a lateral line-inspired MEMS wake detector. The sensing mechanism, design, and fabrication are demonstrated in detail. Experimental results show the detector’s sensitivity is 147 mV·(m/s)⁻¹, and the detection threshold is 0.3 m/s. In addition, the vector test results verify it has vector-detecting capacity. This wake detector can serve AUVs wake detection and tracking technology, which will be promising in AUV positioning and coordinated formation. Full article

(This article belongs to the Special Issue Advances on Autonomous Underwater Vehicles (AUV))

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20 pages, 10653 KiB

Open AccessArticle

Cooperative Navigation Algorithm of Extended Kalman Filter Based on Combined Observation for AUVs

by Guangrun Sheng, Xixiang Liu, Yehua Sheng, Xiangzhi Cheng and Hao Luo

Remote Sens. 2023, 15(2), 533; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15020533 - 16 Jan 2023

Cited by 5 | Viewed by 1900

Abstract

The navigation and positioning of multi-autonomous underwater vehicles (AUVs) in the complex and variable marine environment is a significant and much-needed area of attention, especially considering the fact that cooperative navigation technology is the essential method for multiple AUVs to solve positioning problems. When the extended Kalman filter (EKF) is applied for underwater cooperative localization, the outliers in the sensor observations cause unknown errors in the measurement system due to deep-sea environmental factors, which are difficult to calibrate and cause a significant reduction in the co-location accuracy of AUVs, and can even cause problems with a divergence of estimation error. In this paper, we proposed a cooperative navigation method of the EKF algorithm based on the combined observation of multiple AUVs. Firstly, the corresponding cooperative navigation model is established, and the corresponding measurement model is designed. Then, the EKF model based on combined observation is designed and constructed, and the unknown error is eliminated by introducing a previously measured value. Finally, simulation tests and lake experiments are designed to verify the effectiveness of the algorithm. The results indicate that the EKF algorithm based on combined observation can approximately eliminate errors and improve the accuracy of cooperative localization when the unknown measurement error cannot be calibrated by common EKF methods. The effect of state estimation is improved, and the accuracy of co-location can be effectively improved to avoid serious declines in—and divergence of—estimation accuracy. Full article

(This article belongs to the Special Issue Advances on Autonomous Underwater Vehicles (AUV))

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22 pages, 8106 KiB

Open AccessArticle

A Multi-Robot Coverage Path Planning Method for Maritime Search and Rescue Using Multiple AUVs

by Chang Cai, Jianfeng Chen, Qingli Yan and Fen Liu

Remote Sens. 2023, 15(1), 93; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15010093 - 24 Dec 2022

Cited by 13 | Viewed by 2219

Abstract

In this study, we focus on the Multi-robot Coverage Path Planning (MCPP) problem for maritime Search And Rescue (SAR) missions using a multiple Autonomous Underwater Vehicle (AUV) system, with the ultimate purpose of efficiently and accurately discovering the target from sonar images taken by Side-Scan Sonar (SSS) mounted on the AUVs. Considering the specificities of real maritime SAR projects, we propose a novel MCPP method, in which the MCPP problem is transformed into two sub-problems: Area partitioning and single-AUV coverage path planning. The structure of the task area is first defined using Morse decomposition of the spike pattern. The area partitioning problem is then formulated as an AUV ordering problem, which is solved by developing a customized backtracking method to balance the workload and to avoid segmentation of the possible target area. As for the single-AUV coverage path planning problem, the SAR-A* method is adopted, which generates a path that preferentially visits the possible target areas and reduces the number of turns to guarantee the high quality of the resulting sonar images. Simulation results demonstrate that the proposed method can maintain the workload balance and significantly improve the efficiency and accuracy of discovering the target. Moreover, our experimental results indicate that the proposed method is practical and the mentioned specificities are useful for discovering targets. Full article

(This article belongs to the Special Issue Advances on Autonomous Underwater Vehicles (AUV))

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Other

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17 pages, 1846 KiB

Open AccessTechnical Note

Direction of Arrival Estimation of Acoustic Sources with Unmanned Underwater Vehicle Swarm via Matrix Completion

by Liya Xu, Jianjun Huang, Hao Zhang and Bin Liao

Remote Sens. 2022, 14(15), 3790; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14153790 - 06 Aug 2022

Cited by 4 | Viewed by 1506

Abstract

With the rapid development of vibration and noise reduction technologies, underwater target detection is facing great challenges. Particularly, the task of high-resolution direction of arrival (DOA) estimation with sonar array is becoming more and more tough. In recent years, unmanned underwater vehicles (UUVs) have been developed considerably, with the improvements of target localization performance in terms of adaptability, detection range, operation efficiency, and anti-interference ability. Nevertheless, in general, the size of UUV is small such that current passive sonar systems usually have relatively limited localization accuracy, detection distance, and environmental robustness in complex ocean noise. This motivates us to present a new approach to construct a large-aperture virtual array with multiple small-aperture arrays of unmanned underwater vehicle swarm (UUVS) which consists of multiple UUVs in this paper. However, for the UUVS array, the received data could suffer from unobserved and corrupted samples. This makes it challenging to analyze and process large-aperture array data. Towards this end, the matrix completion technique is employed to recover the unobserved and corrupted data for virtual array construction based on the low rank property of array data matrix. The recovered matrix is then exploited for underwater target bearing estimation using the traditional DOA estimation approach. Numerical results verify that the proposed method is capable of detecting underwater targets with high precision and resolution. Full article

(This article belongs to the Special Issue Advances on Autonomous Underwater Vehicles (AUV))

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