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Advances in Autonomous Underwater Robotics Based on Machine Learning II
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Advances in Autonomous Underwater Robotics Based on Machine Learning II

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Physical Oceanography".

Deadline for manuscript submissions: closed (1 December 2023) | Viewed by 2843

Special Issue Editors

Prof. Dr. Antoni Burguera

E-Mail Website
Guest Editor
Departament de Matemàtiques i Informàtica, Universitat de les Illes Balears, Carretera de Valldemossa Km 7.5, 07122 Palma, Illes Balears, Spain
Interests: robotics; localization; mapping; SLAM; underwater; sonar; computer vision; artificial intelligence; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Francisco Bonin-Font

E-Mail Website
Guest Editor
Departament de Matemàtiques i Informàtica, Universitat de les Illes Balears, Carretera de Valldemossa Km 7.5, 07122 Palma, Illes Balears, Spain
Interests: robot vision underwater; mobile robot navigation; localization of underwater robotics; visual simultaneous localization and mapping; convolutional neural networks; underwater inspection and intervention with robots; underwater robotic field applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the use of autonomous or semi-autonomous robots to perform underwater missions has grown rapidly. Tasks such as submersed infrastructure inspection, the monitoring of underwater plants and algae meadows, or general sub-sea mapping strongly benefit from underwater robotics.

Increasing robots’ autonomy is tightly related to the use of artificial intelligence techniques. Among them, machine learning in general and deep learning in particular have shown great potential, though there are few applications exist are specifically targeted to underwater robotics.

The purpose of this Special Issue is to publish innovative research and application-oriented works related to underwater and marine robotics uses of machine learning.

Papers related (but not limited) to the following topics will be taken into consideration:

  • Marine and underwater sensor processing using machine learning and deep learning:
    • Visual: Image enhancement, segmentation and classification; target localization, object detection and tracking; etc.
    • Acoustic: Point cloud/raw acoustic signal segmentation and classification, target localization, etc.
  • Marine and underwater localization/SLAM using machine learning and deep learning:
    • Single-robot loop detection.
    • Multi-session and multi-robot loop detection.
    • Place/scene recognition.
    • SLAM, localization and mapping.
  • Marine and underwater navigation using machine learning and deep learning:
    • Intelligent and adaptive control architectures.
    • Bio-inspired control architectures.
    • Path planning.
  • Marine and underwater robotic design and optimization using machine and deep learning:
    • Design of bionic and robotic fish.
    • Motion control and optimization.

Papers investigating other artificial intelligence fields not necessarily related to machine or deep learning can also be taken into consideration.

Prof. Dr. Antoni Burguera
Dr. Francisco Bonin-Font
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

  • underwater and marine robotics
  • machine learning and deep learning
  • localization, mapping and SLAM
  • navigation and control architectures
  • sensor processing
  • image processing
  • point cloud processing

Published Papers (2 papers)

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23 pages, 6520 KiB  
Article
State Super Sampling Soft Actor–Critic Algorithm for Multi-AUV Hunting in 3D Underwater Environment
by Zhuo Wang, Yancheng Sui, Hongde Qin and Hao Lu
J. Mar. Sci. Eng. 2023, 11(7), 1257; https://doi.org/10.3390/jmse11071257 - 21 Jun 2023
Cited by 2 | Viewed by 1037
Abstract
Reinforcement learning (RL) is known for its efficiency and practicality in single-agent planning, but it faces numerous challenges when applied to multi-agent scenarios. In this paper, a Super Sampling Info-GAN (SSIG) algorithm based on Generative Adversarial Networks (GANs) is proposed to address the [...] Read more.
Reinforcement learning (RL) is known for its efficiency and practicality in single-agent planning, but it faces numerous challenges when applied to multi-agent scenarios. In this paper, a Super Sampling Info-GAN (SSIG) algorithm based on Generative Adversarial Networks (GANs) is proposed to address the problem of state instability in Multi-Agent Reinforcement Learning (MARL). The SSIG model allows a pair of GAN networks to analyze the previous state of dynamic system and predict the future state of consecutive state pairs. A multi-agent system (MAS) can deduce the complete state of all collaborating agents through SSIG. The proposed model has the potential to be employed in multi-autonomous underwater vehicle (multi-AUV) planning scenarios by combining it with the Soft Actor–Critic (SAC) algorithm. Hence, this paper presents State Super Sampling Soft Actor–Critic (S4AC), which is a new algorithm that combines the advantages of SSIG and SAC and can be applied to Multi-AUV hunting tasks. The simulation results demonstrate that the proposed algorithm has strong learning ability and adaptability and has a considerable success rate in hunting the evading target in multiple testing scenarios. Full article
(This article belongs to the Special Issue Advances in Autonomous Underwater Robotics Based on Machine Learning II)
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17 pages, 5094 KiB  
Article
TCRN: A Two-Step Underwater Image Enhancement Network Based on Triple-Color Space Feature Reconstruction
by Sen Lin, Ruihang Zhang, Zemeng Ning and Jie Luo
J. Mar. Sci. Eng. 2023, 11(6), 1221; https://doi.org/10.3390/jmse11061221 - 13 Jun 2023
Cited by 1 | Viewed by 1352
Abstract
The underwater images acquired by marine detectors inevitably suffer from quality degradation due to color distortion and the haze effect. Traditional methods are ineffective in removing haze, resulting in the residual haze being intensified during color correction and contrast enhancement operations. Recently, deep-learning-based [...] Read more.
The underwater images acquired by marine detectors inevitably suffer from quality degradation due to color distortion and the haze effect. Traditional methods are ineffective in removing haze, resulting in the residual haze being intensified during color correction and contrast enhancement operations. Recently, deep-learning-based approaches have achieved greatly improved performance. However, most existing networks focus on the characteristics of the RGB color space, while ignoring factors such as saturation and hue, which are more important to the human visual system. Considering the above research, we propose a two-step triple-color space feature fusion and reconstruction network (TCRN) for underwater image enhancement. Briefly, in the first step, we extract LAB, HSV, and RGB feature maps of the image via a parallel U-net-like network and introduce a dense pixel attention module (DPM) to filter the haze noise of the feature maps. In the second step, we first propose the utilization of fully connected layers to enhance the long-term dependence between high-dimensional features of different color spaces; then, a group structure is used to reconstruct specific spacial features. When applied to the UFO dataset, our method improved PSNR by 0.21% and SSIM by 0.1%, compared with the second-best method. Numerous experiments have shown that our TCRN brings competitive results compared with state-of-the-art methods in both qualitative and quantitative analyses. Full article
(This article belongs to the Special Issue Advances in Autonomous Underwater Robotics Based on Machine Learning II)
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