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Navigation and Detection Fusion for Autonomous Underwater Vehicles
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Navigation and Detection Fusion for Autonomous Underwater Vehicles

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: closed (5 October 2024) | Viewed by 4336

Special Issue Editors

Dr. Daqi Zhu

E-Mail Website
Guest Editor
Research Institute of Underwater Vehicles and Intelligent Systems, University of Shanghai for Science and Technology, #516 JunGong Road, Shanghai, China
Interests: autonomous underwater vehicle and multi-sensor fusion; fault diagnosis
Special Issues, Collections and Topics in MDPI journals
Dr. Jianjun Ni

E-Mail Website
Guest Editor
College of Artificial Intelligence and Automation, Hohai University. No.1915 Hehai Avenue, Jintan District, Changzhou, China
Interests: autonomous robotics; machine intelligence; multi-agent system

Special Issue Information

Dear Colleagues,

The "Navigation and Detection Fusion for Autonomous Underwater Vehicles" Special Issue tackles critical challenges and offers innovative solutions in the realm of autonomous underwater vehicle (AUV) navigation and object detection. AUVs are pivotal in a range of marine applications, including environmental monitoring, scientific research, and defense. This compilation of articles delves into the integration of navigation and detection systems to bolster AUV autonomy and efficiency. Explored topics encompass sensor fusion techniques, cutting-edge sonar and imaging technologies, path planning algorithms, and machine learning methods aimed at optimizing underwater exploration and data collection. The research presented in this special issue is pivotal for enhancing AUV capabilities and performance across a spectrum of underwater missions, rendering it an invaluable resource for researchers, engineers, and organizations engaged in marine technology and exploration.

This Special Issue aims to share relevant scientific work focused on everything from large-scale patterns to detailed aspects and case studies, encouraging the publication of new emerging information that contributes to knowledge in the field of navigation in general, focusing on but not limited to detection fusion for Autonomous Underwater Vehicles.

Dr. Daqi Zhu
Dr. Jianjun Ni
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
  • tracking control
  • underwater image processing
  • information fusion
  • underwater target recognition
  • Multi-AUV

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Published Papers (3 papers)

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Research

16 pages, 2129 KiB  
Article
A Novel Positional Calibration Method for an Underwater Acoustic Beacon Array Based on the Equivalent Virtual Long Baseline Positioning Model
by Ge Zhang, Guoxing Yi, Zhennan Wei, Yangguang Xie and Ziyang Qi
J. Mar. Sci. Eng. 2024, 12(5), 825; https://doi.org/10.3390/jmse12050825 - 15 May 2024
Viewed by 888
Abstract
The performance of long baseline (LBL) positioning systems is significantly impacted by the distribution and positional calibration accuracy of underwater acoustic beacon arrays. In previous calibration methods for beacon arrays based on autonomous underwater vehicle (AUV) platforms, the slant range information of each [...] Read more.
The performance of long baseline (LBL) positioning systems is significantly impacted by the distribution and positional calibration accuracy of underwater acoustic beacon arrays. In previous calibration methods for beacon arrays based on autonomous underwater vehicle (AUV) platforms, the slant range information of each beacon was processed independently, and each beacon was calibrated one at a time. This approach not only decreases the calibration efficiency but also leaves the positional calibration accuracy of each beacon highly susceptible to the navigation trajectory of the AUV. To overcome these limitations, an equivalent virtual LBL (EVLBL) positioning model is introduced in this paper. This model operates by adjusting the positions of each beacon according to the dead reckoning increments computed during the AUV’s reception of positioning signals, effectively forming a virtual beacon array. Consequently, the AUV is capable of mitigating LBL positioning errors that arise from its motion by simultaneously receiving positioning signals from all beacons. Additionally, an overall calibration method for beacon arrays based on particle swarm optimization (PSO) is proposed. In this approach, the minimization of the deviation between the EVLBL trajectory and the dead reckoning trajectory is set as the optimization objective, and the coordinates of each beacon are iteratively optimized. The simulation results demonstrate that the proposed EVLBL-based PSO algorithm (EVPSO) significantly enhanced the calibration efficiency and positional accuracy of the beacon array. Compared with conventional methods, the estimation error of the beacon positions was reduced from 6.40 m to within 1.00 m. After compensating for the beacon array positions, the positioning error of the LBL system decreased from approximately 5.00 m (with conventional methods) to around 1.00 m (with EVPSO), demonstrating the effectiveness of the proposed approach. Full article
(This article belongs to the Special Issue Navigation and Detection Fusion for Autonomous Underwater Vehicles)
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19 pages, 6152 KiB  
Article
YOLOv7t-CEBC Network for Underwater Litter Detection
by Xinyu Zhang, Daqi Zhu and Wenyang Gan
J. Mar. Sci. Eng. 2024, 12(4), 524; https://doi.org/10.3390/jmse12040524 - 22 Mar 2024
Cited by 3 | Viewed by 1342
Abstract
The issue of marine litter has been an important concern for marine environmental protection for a long time, especially underwater litter. It is not only challenging to clean up, but its prolonged presence underwater can cause damage to marine ecosystems and biodiversity. This [...] Read more.
The issue of marine litter has been an important concern for marine environmental protection for a long time, especially underwater litter. It is not only challenging to clean up, but its prolonged presence underwater can cause damage to marine ecosystems and biodiversity. This has led to underwater robots equipped with powerful visual detection algorithms becoming the mainstream alternative to human labor for cleaning up underwater litter. This study proposes an enhanced underwater litter detection algorithm, YOLOv7t-CEBC, based on YOLOv7-tiny, to assist underwater robots in target identification. The research introduces some modules tailored for marine litter detection within the model framework, addressing inter-class similarity and intra-class variability inherent in underwater waste while balancing detection precision and speed. Experimental results demonstrate that, on the Deep Plastic public dataset, YOLOv7t-CEBC achieves a detection accuracy (mAP) of 81.8%, markedly surpassing common object detection algorithms. Moreover, the detection frame rate reaches 118 FPS, meeting the operational requirements of underwater robots. The findings affirm that the enhanced YOLOv7t-CEBC network serves as a reliable tool for underwater debris detection, contributing to the maintenance of marine health. Full article
(This article belongs to the Special Issue Navigation and Detection Fusion for Autonomous Underwater Vehicles)
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19 pages, 2507 KiB  
Article
Bio-Inspired Cooperative Control Scheme of Obstacle Avoidance for UUV Swarm
by Zhao Wang, Hongjian Wang, Jianya Yuan, Dan Yu, Kai Zhang and Jingfei Ren
J. Mar. Sci. Eng. 2024, 12(3), 489; https://doi.org/10.3390/jmse12030489 - 14 Mar 2024
Cited by 1 | Viewed by 1267
Abstract
The complex underwater environment poses significant challenges for unmanned underwater vehicles (UUVs), particularly in terms of communication constraints and the need for precise cooperative obstacle avoidance and trajectory tracking. Addressing these challenges solely through position information is crucial in this field. This study [...] Read more.
The complex underwater environment poses significant challenges for unmanned underwater vehicles (UUVs), particularly in terms of communication constraints and the need for precise cooperative obstacle avoidance and trajectory tracking. Addressing these challenges solely through position information is crucial in this field. This study explores the intricate task of managing a group of UUVs as they navigate obstacles and follow a given trajectory, all based on position information. A new dynamic interactive topology framework utilizing sonar technology has been developed for the UUVs. This framework not only provides position information for the UUV swarm but also for the surrounding obstacles, enhancing situational awareness. Additionally, a bio-inspired cooperative control strategy designed for UUV swarms utilizing sonar interaction topology is introduced. This innovative method eliminates the need for velocity data from neighboring UUVs, instead relying solely on position information to achieve swarm cooperative control, obstacle avoidance, and trajectory adherence. The effectiveness of this method is validated through extensive simulations. The results show that the proposed method demonstrates improved sensitivity in obstacle detection, enabling faster trajectory tracking while maintaining a safer distance compared to traditional methods. Ultimately, this innovative strategy not only enhances operational efficiency but also enhances safety measures in UUV swarm operations. Full article
(This article belongs to the Special Issue Navigation and Detection Fusion for Autonomous Underwater Vehicles)
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