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Applications of Marine Vehicles in Maritime Environments
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Applications of Marine Vehicles in Maritime Environments

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

Deadline for manuscript submissions: closed (5 September 2023) | Viewed by 7329

Special Issue Editors

Dr. Dimitrios V. Lyridis

E-Mail Website
Guest Editor
Laboratory for Maritime Transport, School of Naval Architecture and Marine Engineering, National Technical University of Athens, 15780 Athens, Greece
Interests: maritime transport; economics and finance; energy and the environment
Special Issues, Collections and Topics in MDPI journals
Dr. Charis Ntakolia

E-Mail Website
Guest Editor
Laboratory for Maritime Transport, School of Naval Architecture and Marine Engineering, National Technical University of Athens, 15780 Athens, Greece
Interests: applied mathematical sciences; optimization techniques; operational research techniques; numerical analysis; linear and non-linear programming and machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Marine vehicles have gained significant attention in recent years, leading to their use in various applications for surveillance, monitoring, inspection, and delivery, among others. For their efficient adoption and operation in dynamic and complex environments, particularly without human intervention, innovative solutions should be investigated for guidance, navigation, and control. This Special Issue aims at collecting papers on new findings and review papers dealing with the automated operation and applications of marine vehicles. Potential topics of interest for publication include the following:

  • Unmanned surface or underwater vehicles, autonomous surface or underwater vehicles, remotely operated vehicles, or underwater gliders;
  • Swarms of unmanned marine, cooperative surface, and underwater vehicles;
  • Guidance, navigation, and path planning;
  • Vehicle model tests, applications, case studies, field trials, and experimental results;
  • Machine learning methods and their applications in marine vehicles;
  • Path following, path planning, trajectory planning, and automatic collision avoidance;
  • Intelligent and adaptive control architecture;
  • Obstacle detection and avoidance systems.

Dr. Dimitrios V. Lyridis
Dr. Charis Ntakolia
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.

Published Papers (4 papers)

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Research

25 pages, 11761 KiB  
Article
A New Coastal Crawler Prototype to Expand the Ecological Monitoring Radius of OBSEA Cabled Observatory
by Ahmad Falahzadeh, Daniel Mihai Toma, Marco Francescangeli, Damianos Chatzievangelou, Marc Nogueras, Enoc Martínez, Matias Carandell, Michael Tangerlini, Laurenz Thomsen, Giacomo Picardi, Marie Le Bris, Luisa Dominguez, Jacopo Aguzzi and Joaquin del Río
J. Mar. Sci. Eng. 2023, 11(4), 857; https://doi.org/10.3390/jmse11040857 - 18 Apr 2023
Cited by 3 | Viewed by 1345
Abstract
The use of marine cabled video observatories with multiparametric environmental data collection capability is becoming relevant for ecological monitoring strategies. Their ecosystem surveying can be enforced in real time, remotely, and continuously, over consecutive days, seasons, and even years. Unfortunately, as most observatories [...] Read more.
The use of marine cabled video observatories with multiparametric environmental data collection capability is becoming relevant for ecological monitoring strategies. Their ecosystem surveying can be enforced in real time, remotely, and continuously, over consecutive days, seasons, and even years. Unfortunately, as most observatories perform such monitoring with fixed cameras, the ecological value of their data is limited to a narrow field of view, possibly not representative of the local habitat heterogeneity. Docked mobile robotic platforms could be used to extend data collection to larger, and hence more ecologically representative areas. Among the various state-of-the-art underwater robotic platforms available, benthic crawlers are excellent candidates to perform ecological monitoring tasks in combination with cabled observatories. Although they are normally used in the deep sea, their high positioning stability, low acoustic signature, and low energetic consumption, especially during stationary phases, make them suitable for coastal operations. In this paper, we present the integration of a benthic crawler into a coastal cabled observatory (OBSEA) to extend its monitoring radius and collect more ecologically representative data. The extension of the monitoring radius was obtained by remotely operating the crawler to enforce back-and-forth drives along specific transects while recording videos with the onboard cameras. The ecological relevance of the monitoring-radius extension was demonstrated by performing a visual census of the species observed with the crawler’s cameras in comparison to the observatory’s fixed cameras, revealing non-negligible differences. Additionally, the videos recorded from the crawler’s cameras during the transects were used to demonstrate an automated photo-mosaic of the seabed for the first time on this class of vehicles. In the present work, the crawler travelled in an area of 40 m away from the OBSEA, producing an extension of the monitoring field of view (FOV), and covering an area approximately 230 times larger than OBSEA’s camera. The analysis of the videos obtained from the crawler’s and the observatory’s cameras revealed differences in the species observed. Future implementation scenarios are also discussed in relation to mission autonomy to perform imaging across spatial heterogeneity gradients around the OBSEA. Full article
(This article belongs to the Special Issue Applications of Marine Vehicles in Maritime Environments)
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17 pages, 3000 KiB  
Article
Path Planning in the Case of Swarm Unmanned Surface Vehicles for Visiting Multiple Targets
by Charis Ntakolia and Dimitrios V. Lyridis
J. Mar. Sci. Eng. 2023, 11(4), 719; https://doi.org/10.3390/jmse11040719 - 26 Mar 2023
Cited by 1 | Viewed by 1454
Abstract
In this study, we present a hybrid approach of Ant Colony Optimization algorithm (ACO) with fuzzy logic and clustering methods to solve multiobjective path planning problems in the case of swarm Unmanned Surface Vehicles (USVs). This study aims to further explore the performance [...] Read more.
In this study, we present a hybrid approach of Ant Colony Optimization algorithm (ACO) with fuzzy logic and clustering methods to solve multiobjective path planning problems in the case of swarm Unmanned Surface Vehicles (USVs). This study aims to further explore the performance of the ACO algorithm by integrating fuzzy logic in order to cope with the multiple contradicting objectives and generate quality solutions by in-parallel identifying the mission areas of each USV to reach the desired targets. The design of the operational areas for each USV in the swarm is performed by a comparative evaluation of three popular clustering algorithms: Mini Batch K-Means, Ward Clustering and Birch. Following the identification of the operational areas, the design of each USV path to perform the operation is performed based on the minimization of traveled distance and energy consumption, as well as the maximization of path smoothness. To solve this multiobjective path planning problem, a comparative evaluation is conducted among ACO and fuzzy inference systems, Mamdani (ACO-Mamdani) and Takagi–Sugeno–Kang (ACO-TSK). The results show that depending on the needs of the application, each methodology can contribute, respectively. ACO-Mamdani generates better paths, but ACO-TSK presents higher computation efficiency. Full article
(This article belongs to the Special Issue Applications of Marine Vehicles in Maritime Environments)
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19 pages, 3603 KiB  
Article
Lightweight Underwater Target Detection Algorithm Based on Dynamic Sampling Transformer and Knowledge-Distillation Optimization
by Liang Chen, Yuyi Yang, Zhenheng Wang, Jian Zhang, Shaowu Zhou and Lianghong Wu
J. Mar. Sci. Eng. 2023, 11(2), 426; https://doi.org/10.3390/jmse11020426 - 15 Feb 2023
Cited by 4 | Viewed by 2244
Abstract
Underwater robot perception is a critical task. Due to the complex underwater environment and low quality of optical images, it is difficult to obtain accurate and stable target position information using traditional methods, making it unable to meet practical use requirements. The relatively [...] Read more.
Underwater robot perception is a critical task. Due to the complex underwater environment and low quality of optical images, it is difficult to obtain accurate and stable target position information using traditional methods, making it unable to meet practical use requirements. The relatively low computing power of underwater robots prevents them from supporting real-time detection with complex model algorithms for deep learning. To resolve the above problems, a lightweight underwater target detection and recognition algorithm based on knowledge distillation optimization is proposed based on the YOLOv5-lite model. Firstly, a dynamic sampling Transformer module is proposed. After the feature matrix is sparsely sampled, the query matrix is dynamically shifted to achieve the purpose of targeted attention modeling. Additionally, the shared kernel parameter convolution is used to optimize the matrix encoding and simplify the forward-propagation memory overhead. Then, a distillation method with decoupled localization and recognition is designed in the model-training process. The ability to transfer the effective localization knowledge of the positive sample boxes is enhanced, which ensures that the model maintains the same number of parameters to improve the detection accuracy. Validated by real offshore underwater image data, the experimental results show that our method provides an improvement of 6.6% and 5.0% over both baseline networks with different complexity models under the statistical index of detection accuracy mAP, which also suggests 58.8% better efficiency than models such as the standard YOLOv5. Through a comparison with other mainstream single-stage networks, the effectiveness and sophistication of the proposed algorithm are validated. Full article
(This article belongs to the Special Issue Applications of Marine Vehicles in Maritime Environments)
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17 pages, 4040 KiB  
Article
Underwater Target Detection Lightweight Algorithm Based on Multi-Scale Feature Fusion
by Liang Chen, Yuyi Yang, Zhenheng Wang, Jian Zhang, Shaowu Zhou and Lianghong Wu
J. Mar. Sci. Eng. 2023, 11(2), 320; https://doi.org/10.3390/jmse11020320 - 02 Feb 2023
Cited by 6 | Viewed by 1876
Abstract
The performance of underwater target detection algorithms is affected by poor imaging quality in underwater environments. Due to the arithmetic power limitation of underwater devices, existing deep learning networks are unable to provide efficient detection processes with high detection accuracy. Lightweight CNN models [...] Read more.
The performance of underwater target detection algorithms is affected by poor imaging quality in underwater environments. Due to the arithmetic power limitation of underwater devices, existing deep learning networks are unable to provide efficient detection processes with high detection accuracy. Lightweight CNN models have been actively applied for underwater environment detection, yet their lite feature fusion networks cannot provide effective fusion effects and reduce the detection accuracy. In this paper, a lightweight algorithm based on multi-scale feature fusion was proposed, with the model parameters greatly reduced, improving the target detection accuracy. The forward propagation memory overhead is reduced by using multi-scale shared convolutional kernels and pooling operations to co-construct the query matrix in the Tansformer encoding stage. Then, the feature fusion path is optimized in order to enhance the connection of multi-scale features. A multiscale feature adaptive fusion strategy is used to enhance the detection performance and reduce the dependence on the complex feature extraction network. The feature extraction network is also reparameterized to simplify the operation. Using the UPRC offshore dataset for validation, the study results have demonstrated that the statistical mAP metrics validate the detection accuracy. Compared with SSD, RetinaNet and YOLOv5-s improved by 13%, 8.6%, and 0.8%, while the number of parameters decreased by 76.09%, 89.74%, and 87.67%. In addition, compared with the YOLOv5-lite model algorithm with the same parameter volume, the mAP is improved by 3.8%, which verifies the accuracy and efficiency of the algorithm in this paper. Full article
(This article belongs to the Special Issue Applications of Marine Vehicles in Maritime Environments)
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