Topic Editors

1. Instituto de Cièncias del Mar (ICM-CSIC), Renewable Marine Resources ("Functioning and Vulnerability of Marine Ecosystems" Group), I E-08003 Barcelona, Spain
2. Stazione Zoologica Anton Dohrn, 80122 Naples, Italy
The BioRobotics Institute, Scuola Superiore Sant’Anna, 56127 Pisa, Italy
Instituto de Cièncias del Mar (ICM-CSIC), Renewable Marine Resources (Functioning and Vulnerability of Marine Ecosystems" Group), I E-08003 Barcelona, Spain
1. Institute of Marine Sciences (ISMAR) of the National Research Council (CNR), Lerici, Italy
2. Stazione Zoologica Anton Dohrn (SZN), Naples, Italy
Dr. Sascha Flögel
GEOMAR - Helmholtz-Zentrum für Ozeanforschung Kieldisabled, Kiel, Germany
Stazione Zoologica Anton Dohrn, 80122 Naples, Italy
Dr. Peter Weiss
Spartan Space, 13275 Marseille, France
Sarti Research Group, Electronics Engineering Department, Universitat Politècnica de Catalunya, 08800 Vilanova i la Geltrú, Spain

Advanced Sensors and Robotic Platform Technologies for the Exploration and Monitoring of Marine Ecosystems and Far Beyond

Abstract submission deadline
closed (31 January 2023)
Manuscript submission deadline
closed (30 April 2023)
Viewed by
57560

Topic Information

Dear Colleagues,

There is a growing need for autonomous monitoring of oceans and their ecosystems in spite of climate change and rising industrial activities (e.g., oil/gas and mining extractions or fisheries). The current state-of-the-art in robotics can provide marine scientists with tools to explore and monitor ecosystems in their pristine or impacted or recovering state. There is an existing debate on which best sensor and platform solutions should be used and cooperatively combined to acquire representative multidisciplinary biological and environmental data. Moreover, due to the presence of oceans in solar systems (e.g., on icy moons of Enceladus and Europa), there may be room for the application of such monitoring solutions to the exploration of extraterrestrial life, enhancing the cooperation between marine and space agencies. In this scenario, we call for contributions on:

  • Innovative sensors and their organization into smart packages for the augmented monitoring of species presence (e.g., eDNA and Passive Acoustic Monitoring, detecting organisms beyond optoacoustic imaging technologies) and their environment;
  • Classic and innovative biomimetic platform designs for pelagic, benthic, surface and aerial exploration;
  • New energy forms to increase operational autonomy of platforms;
  • Artificial Intelligence (AI) and onboard data processing for autonomous navigation and transmission strategies;
  • Web solutions and architectures for data ingestion, storage and displaying in real or near-real-time;
  • Web statistic tools for the online interactive time-series and multivariate statistical analysis by end-users;
  • Data Science applications and relationships with large international data repositories.

Dr. Jacopo Aguzzi
Dr. Giacomo Picardi
Dr. Damianos Chatzievangelou
Dr. Simone Marini
Dr. Sascha Flögel
Dr. Sergio Stefanni
Dr. Peter Weiss
Dr. Daniel Mihai Toma
Topic Editors

Keywords

  • marine robotics
  • ocean exploration
  • ecological montioring
  • biomimicking platforms
  • fuel cells
  • optoacoutsic imaging
  • omics sensors
  • mission autonomy
  • edge computing
  • data transmission
  • web portals
  • space exploration
  • exo-oceans

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Automation
automation
- 2.9 2020 20.6 Days CHF 1000
Journal of Marine Science and Engineering
jmse
2.7 4.4 2013 16.9 Days CHF 2600
Machines
machines
2.1 3.0 2013 15.6 Days CHF 2400
Robotics
robotics
2.9 6.7 2012 17.7 Days CHF 1800
Sensors
sensors
3.4 7.3 2001 16.8 Days CHF 2600

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Published Papers (23 papers)

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15 pages, 326 KiB  
Article
Multi-Sensor Data Fusion Method Based on Improved Evidence Theory
by Shuanghu Qiao, Yunsheng Fan, Guofeng Wang and Haoyan Zhang
J. Mar. Sci. Eng. 2023, 11(6), 1142; https://doi.org/10.3390/jmse11061142 - 30 May 2023
Cited by 4 | Viewed by 1499
Abstract
To achieve autonomous navigation in complex marine environments, unmanned surface vehicles are equipped with a variety of sensors for sensing the surrounding environment and their own state. To address the issue of unsatisfactory multi-sensor information fusion in stochastic uncertain systems with unknown disturbances, [...] Read more.
To achieve autonomous navigation in complex marine environments, unmanned surface vehicles are equipped with a variety of sensors for sensing the surrounding environment and their own state. To address the issue of unsatisfactory multi-sensor information fusion in stochastic uncertain systems with unknown disturbances, an improved evidence theory multi-sensor data fusion method is proposed in this article. First, the affiliation function in fuzzy set theory is introduced as a support function to assign initial evidence for multi-sensor data, and the initial evidence is corrected according to the degree of data bias. Second, a divergence measure is employed to measure the degree of conflict and discrepancy among the evidence, and each piece of evidence is allocated proportional weight based on the conflict allocation principle. Finally, the evidence is synthesized through the evidence combination rule, and the data are weighted and summed to obtain the data fusion results. Since it is difficult to obtain dynamic information from multiple sensors carried by unmanned surface vehicles in practical applications, and considering that the proposed method has universal applicability, practical application experiments using previous research demonstrate that the proposed method has higher fusion accuracy than other existing data fusion methods. Full article
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14 pages, 2543 KiB  
Article
Lead-Free Cs3Bi2Br9 Perovskite Quantum Dots for Detection of Heavy Metal Cu2+ Ions in Seawater
by Yuefeng Gao and Baojiu Chen
J. Mar. Sci. Eng. 2023, 11(5), 1001; https://doi.org/10.3390/jmse11051001 - 8 May 2023
Cited by 3 | Viewed by 1861
Abstract
Seawater pollution caused by heavy metal ions is a growing concern among the public. Perovskite quantum dots (PeQDs) are ideal probes for detecting metal ions due to their exceptional sensing characteristics, including remarkable sensitivity, low detection limit, and good selectivity. However, traditional lead-based [...] Read more.
Seawater pollution caused by heavy metal ions is a growing concern among the public. Perovskite quantum dots (PeQDs) are ideal probes for detecting metal ions due to their exceptional sensing characteristics, including remarkable sensitivity, low detection limit, and good selectivity. However, traditional lead-based PeQDs exhibit drawbacks related to lead toxicity and poor water stability. Herein, lead-free halide PeQDs Cs3Bi2Br9 were synthesized using Bi3+ instead of Pb2+ via the ligand-assisted reprecipitation method. The luminescence performance of the precursor is analyzed with respect to the reaction conditions. The results reveal that the optimal reaction temperature is 80 °C, the ideal octylamine dosage is 35 μL, and the most effective reaction time is 10 min. Photoluminescence spectra of Cs3Bi2Br9 are analyzed at various temperatures and demonstrate that fluorescence intensity decreases as temperature increases. The value of the exciton binding energy (Eb) is determined to be 88.6 meV. Cs3Bi2Br9 PeQDs synthesized under the optimum reaction conditions are utilized as fluorescent probes to detect copper ions in seawater. Results from experiments demonstrate that the presence of copper ions markedly quenched the photoluminescence of Cs3Bi2Br9 owing to the effective transfer of electrons from Cs3Bi2Br9 to Cu2+. A strong linear correlation between the degree of quenching and the contents of Cu2+ is observed. Cs3Bi2Br9 PeQDs demonstrate a sensitivity and detection limit of 1.21 μM−1 and 98.3 nM, respectively. Furthermore, this probe exhibits good photostability, water stability, and selectivity for copper ions, thereby indicating its potential for detecting marine heavy metal contaminants. Full article
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19 pages, 4779 KiB  
Article
Integrated Underwater Detection and Communication System Based on P4 Code-Modulated OFDM Signal
by Xinglong Feng, Zhenyi Zhao, Yuzhong Zhang and Qiao Hu
J. Mar. Sci. Eng. 2023, 11(5), 920; https://doi.org/10.3390/jmse11050920 - 25 Apr 2023
Cited by 2 | Viewed by 1415
Abstract
Because of the requirements of marine resources exploration and underwater cooperative operation, the underwater operation of common detection and communication equipment is difficult and unreliable. Therefore, it is urgent to establish an accurate underwater target detection and multi-node communication integrated system. An integrated [...] Read more.
Because of the requirements of marine resources exploration and underwater cooperative operation, the underwater operation of common detection and communication equipment is difficult and unreliable. Therefore, it is urgent to establish an accurate underwater target detection and multi-node communication integrated system. An integrated method of underwater electric field detection and communication based on P4 code-modulated OFDM signal is proposed in response to the above requirements. The working principle, system structure, and signal processing of underwater electric field detection and electric field communication are similar. This article uses detection signal P4 code to modulate the phase of OFDM communication signal, thus realizing the integration of Underwater Detection and Communication System (IUDCS). The simulation results show that IUDCS can meet the detection range and velocity characteristics of underwater dynamic targets. The simulation results also meet the requirements for communication error rate and symbol error rate, thereby verifying the performance of underwater electric field IUDCS. This method is necessary for the realization of IUDCS in terms of reducing platform size, reducing power consumption, and enhancing concealment. Moreover, it shows good application prospects in underwater robot cluster operations. Full article
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15 pages, 3060 KiB  
Article
An Effective Multi-Layer Attention Network for SAR Ship Detection
by Zhiling Suo, Yongbo Zhao and Yili Hu
J. Mar. Sci. Eng. 2023, 11(5), 906; https://doi.org/10.3390/jmse11050906 - 23 Apr 2023
Cited by 3 | Viewed by 1444
Abstract
The use of deep learning-based techniques has improved the performance of synthetic aperture radar (SAR) image-based applications, such as ship detection. However, all existing methods have limited object detection performance under the conditions of varying ship sizes and complex background noise, to the [...] Read more.
The use of deep learning-based techniques has improved the performance of synthetic aperture radar (SAR) image-based applications, such as ship detection. However, all existing methods have limited object detection performance under the conditions of varying ship sizes and complex background noise, to the best of our knowledge. In this paper, to solve both the multi-scale problem and the noisy background issues, we propose a multi-layer attention approach based on the thorough analysis of both location and semantic information. The solution works by exploring the richness of spatial information of the low-level feature maps generated by a backbone and the richness of semantic information of the high-level feature maps created by the same method. Additionally, we integrate an attention mechanism into the network to exclusively extract useful features from the input maps. Tests involving multiple SAR datasets show that our proposed solution enables significant improvements to the accuracy of ship detection regardless of vessel size and background complexity. Particularly for the widely-adopted High-Resolution SAR Images Dataset (HRSID), the new method provides a 1.3% improvement in the average precision for detection. The proposed new method can be potentially used in other feature-extraction-based classification, detection, and segmentation. Full article
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20 pages, 3091 KiB  
Article
Efficient Underwater Acoustical Localization Method Based on TDOA with Sensor Position Errors
by Ying Liu, Yingmin Wang and Cheng Chen
J. Mar. Sci. Eng. 2023, 11(4), 861; https://doi.org/10.3390/jmse11040861 - 19 Apr 2023
Cited by 5 | Viewed by 1421
Abstract
Underwater acoustic localization (UWAL) is extremely challenging due to the multipath nature of extreme underwater environments, the sensor position uncertainty caused by unpredictable ocean currents, and the lack of underwater observation data due to sparse array, which all affect localization performance. Addressing these [...] Read more.
Underwater acoustic localization (UWAL) is extremely challenging due to the multipath nature of extreme underwater environments, the sensor position uncertainty caused by unpredictable ocean currents, and the lack of underwater observation data due to sparse array, which all affect localization performance. Addressing these issues, this paper proposes a simple and effective underwater acoustic localization method using the time difference of arrival (TDOA) measurements based on the multipath channel effect of the underwater environment. By introducing the calibration source, localization performance was improved, and the sensor position error was corrected. The Cramér–Rao lower bound (CRLB) was derived, and the proposed method was able to achieve the CRLB with small deviation. Numerical simulations confirm the improved performance of the proposed method, including (1) a 20 dB and 30 dB reduction in the CRLB for far and near source scenarios, respectively, indicating improved accuracy and reliability when estimating unknown sources; (2) better Mean Squared Error (MSE) performance compared to existing methods and an efficiency of over 90% in low noise and above 80% in moderate noise in several scenarios, with a delayed threshold effect; and (3) achieving CRLB performance with only three sensors in a 3D space, even under moderate noise, while existing methods require at least five sensors for comparable performance. Our results demonstrate the efficacy of the proposed method in enhancing the accuracy and efficiency of source localization. Full article
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15 pages, 6600 KiB  
Article
User-Driven Design and Development of an Underwater Soft Gripper for Biological Sampling and Litter Collection
by Giacomo Picardi, Mauro De Luca, Giovanni Chimienti, Matteo Cianchetti and Marcello Calisti
J. Mar. Sci. Eng. 2023, 11(4), 771; https://doi.org/10.3390/jmse11040771 - 31 Mar 2023
Cited by 3 | Viewed by 2011
Abstract
Implementing manipulation and intervention capabilities in underwater vehicles is of crucial importance for commercial and scientific reasons. Mainstream underwater grippers are designed for the heavy load tasks typical of the industrial sector; however, due to the lack of alternatives, they are frequently used [...] Read more.
Implementing manipulation and intervention capabilities in underwater vehicles is of crucial importance for commercial and scientific reasons. Mainstream underwater grippers are designed for the heavy load tasks typical of the industrial sector; however, due to the lack of alternatives, they are frequently used in biological sampling applications to handle irregular, delicate, and deformable specimens with a consequent high risk of damage. To overcome this limitation, the design of grippers for marine science applications should explicitly account for the requirements of end-users. In this paper, we aim at making a step forward and propose to systematically account for the needs of end-users by resorting to design tools used in industry for the conceptualization of new products which can yield great benefits to both applied robotic research and marine science. After the generation of the concept design for the gripper using a reduced version of the House of Quality and the Pugh decision matrix, we reported on its mechanical design, construction, and preliminary testing. The paper reports on the full design pipeline from requirements collection to preliminary testing with the aim of fostering and providing structure to fruitful interdisciplinary collaborations at the interface of robotics and marine science. Full article
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19 pages, 384 KiB  
Review
Health-Monitoring Systems for Marine Structures: A Review
by Arturo Silva-Campillo, Francisco Pérez-Arribas and Juan Carlos Suárez-Bermejo
Sensors 2023, 23(4), 2099; https://doi.org/10.3390/s23042099 - 13 Feb 2023
Cited by 5 | Viewed by 4418
Abstract
This paper presents a comprehensive review of the state-of-the-art developments in health monitoring of marine structures. Monitoring the health of marine structures plays a key role in reducing the risk of structural failure. The authors establish the different sensors with their theoretical foundations [...] Read more.
This paper presents a comprehensive review of the state-of-the-art developments in health monitoring of marine structures. Monitoring the health of marine structures plays a key role in reducing the risk of structural failure. The authors establish the different sensors with their theoretical foundations and applications in order to determine the optimal position of the sensors on board. Once the data were collected, it was necessary to use for subsequent treatment; thus, the authors identified the different methodologies related to the treatment of data collected by the sensors. The authors provide a historical review of the location of different sensors depending on the type of ship and offshore platform. Finally, this review paper states the conclusions and future trends of this technology. Full article
19 pages, 5508 KiB  
Article
Features of Oil Spills Monitoring on the Water Surface by the Russian Federation in the Arctic Region
by Artem Alekseevich Khalturin, Konstantin Dmitrievich Parfenchik and Vadim Anatolievich Shpenst
J. Mar. Sci. Eng. 2023, 11(1), 111; https://doi.org/10.3390/jmse11010111 - 5 Jan 2023
Cited by 13 | Viewed by 3307
Abstract
Given that the recent rapid growth of offshore production, especially in the Arctic region of the Russian Federation, is causing increased concern about oil spills on the water surface, this issue is especially relevant and important today. These pollutants have a devastating impact [...] Read more.
Given that the recent rapid growth of offshore production, especially in the Arctic region of the Russian Federation, is causing increased concern about oil spills on the water surface, this issue is especially relevant and important today. These pollutants have a devastating impact on the world’s marine biosphere. Therefore, effective and reliable methods and instruments must be used for operational spill detection in order to detect a remote oil spill. Several methods for oil spill monitoring and Russian developments in this area were described, including their features, advantages, and drawbacks. In cases when use in difficult Arctic conditions was anticipated, due to the harsh climate and ice-covered water surface, it was not always possible for spill detection instruments to be utilized. Despite this, such methods as radar, infrared, and ultraviolet were proven to be effective during this research. Ultimately, the combination of these methods returned the greatest volume of information to offshore platform staff about a detected oil spill. The information provided includes the spread area of the spill, the thickness of the leak, and the chemical composition of the oil. Full article
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24 pages, 15989 KiB  
Article
B-YOLOX-S: A Lightweight Method for Underwater Object Detection Based on Data Augmentation and Multiscale Feature Fusion
by Jun Wang, Shuman Qi, Chao Wang, Jin Luo, Xin Wen and Rui Cao
J. Mar. Sci. Eng. 2022, 10(11), 1764; https://doi.org/10.3390/jmse10111764 - 16 Nov 2022
Cited by 7 | Viewed by 2743
Abstract
With the increasing maturity of underwater agents-related technologies, underwater object recognition algorithms based on underwater robots have become a current hotspot for academic and applied research. However, the existing underwater imaging conditions are poor, the images are blurry, and the underwater robot visual [...] Read more.
With the increasing maturity of underwater agents-related technologies, underwater object recognition algorithms based on underwater robots have become a current hotspot for academic and applied research. However, the existing underwater imaging conditions are poor, the images are blurry, and the underwater robot visual jitter and other factors lead to lower recognition precision and inaccurate positioning in underwater target detection. A YOLOX-based underwater object detection model, B-YOLOX-S, is proposed to detect marine organisms such as echinus, holothurians, starfish, and scallops. First, Poisson fusion is used for data amplification at the input to balance the number of detected targets. Then, wavelet transform is used to perform Style Transfer on the enhanced images to achieve image restoration. The clarity of the images and detection targets is further increased and the generalization of the model is enhanced. Second, a combination of BIFPN-S and FPN is proposed to fuse the effective feature layer obtained by the Backbone layer to enhance the detection precision and accelerate model detection. Finally, the localization loss function of the prediction layer in the network is replaced by EIoU_Loss to heighten the localization precision in detection. Experimental results comparing the B-YOLOX-S algorithm model with mainstream algorithms such as FasterRCNN, YOLOV3, YOLOV4, YOLOV5, and YOLOX on the URPC2020 dataset show that the detection precision and detection speed of the algorithm model have obvious advantages over other algorithm networks. The average detection accuracy mAP value is 82.69%, which is 5.05% higher than the benchmark model (YOLOX-s), and the recall rate is 8.03% higher. Thus, the validity of the algorithmic model proposed in this paper is demonstrated. Full article
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9 pages, 2066 KiB  
Article
Underwater Tone Detection with Robust Coherently-Averaged Power Processor
by Qichen Xie, Cheng Chi, Shenglong Jin, Guanqun Wang, Yu Li and Haining Huang
J. Mar. Sci. Eng. 2022, 10(10), 1505; https://doi.org/10.3390/jmse10101505 - 16 Oct 2022
Cited by 3 | Viewed by 1350
Abstract
The detection of tonal signals with unknown frequencies is an important area of study in underwater signal processing. A common approach to address this issue is to use the Discrete Fourier Transform (DFT) for observations. When a tone does not lie precisely at [...] Read more.
The detection of tonal signals with unknown frequencies is an important area of study in underwater signal processing. A common approach to address this issue is to use the Discrete Fourier Transform (DFT) for observations. When a tone does not lie precisely at the discrete DFT frequency point, its energy will leak to adjacent frequency point. This phenomenon is known as scalloping loss or Picket Fence Effect (PFE). PFE leads to the degradation of detection performance based on DFT. This paper studies the problem of robust detection in the case of PFE. A coherently-averaged power processor utilizing the information of adjacent frequency bins is designed. The results of simulations and experiments show that the proposed method is robust against PFE, and is highly suitable for tone detection in practical circumstances. Full article
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20 pages, 10074 KiB  
Article
Ocean Plankton Biomass Estimation with a Digital Holographic Underwater Glider
by Yingjie Wang, Lianhong Zhang, Wei Ma, Yanhui Wang, Wendong Niu, Yu Song and Weimin Wang
J. Mar. Sci. Eng. 2022, 10(9), 1258; https://doi.org/10.3390/jmse10091258 - 6 Sep 2022
Cited by 2 | Viewed by 1959
Abstract
Accurate quantitative plankton observation is significant for biogeochemistry and environmental monitoring. However, current observation equipment is mostly shipborne, and there is a lack of long-term, large-scale, and low-cost methods for plankton observation. This paper proposes a solution to investigate plankton using a Seascan [...] Read more.
Accurate quantitative plankton observation is significant for biogeochemistry and environmental monitoring. However, current observation equipment is mostly shipborne, and there is a lack of long-term, large-scale, and low-cost methods for plankton observation. This paper proposes a solution to investigate plankton using a Seascan holographic camera equipped with a “Petrel-II” underwater glider for a longer time sequence and at a larger scale. Aiming at the new challenges of low efficiency and low accuracy of holographic image processing after integrating holographic imaging systems and underwater gliders, a novel plankton data analysis method applicable to Digital Holographic Underwater Gliders (DHUG) is proposed. The algorithm has the following features: (1) high efficiency: the algorithm breaks the traditional hologram information extraction order, focusing only on the key regions in the hologram and minimizing the redundant computation; (2) high accuracy: applying the Sobel variance algorithm to the plankton in the hologram to focus the plane extraction significantly improves the focus accuracy; and (3) high degree of automation: by integrating a convolutional neural network, the algorithm achieves a fully automated analysis of the observed data. A sea test in the South China Sea verified that the proposed algorithm could greatly improve the problems of severe plankton segmentation and the low focusing accuracy of traditional information extraction algorithms. It also proved that the DHUG plankton survey has great potential. Full article
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13 pages, 4060 KiB  
Article
An Improved YOLOV5 Based on Triplet Attention and Prediction Head Optimization for Marine Organism Detection on Underwater Mobile Platforms
by Yan Li, Xinying Bai and Chunlei Xia
J. Mar. Sci. Eng. 2022, 10(9), 1230; https://doi.org/10.3390/jmse10091230 - 2 Sep 2022
Cited by 11 | Viewed by 2958
Abstract
Machine vision-based automatic detection of marine organisms is a fundamental task for the effective analysis of production and habitat changes in marine ranches. However, challenges of underwater imaging, such as blurring, image degradation, scale variation of marine organisms, and background complexity, have limited [...] Read more.
Machine vision-based automatic detection of marine organisms is a fundamental task for the effective analysis of production and habitat changes in marine ranches. However, challenges of underwater imaging, such as blurring, image degradation, scale variation of marine organisms, and background complexity, have limited the performance of image recognition. To overcome these issues, underwater object detection is implemented by an improved YOLOV5 with an attention mechanism and multiple-scale detection strategies for detecting four types of common marine organisms in the natural scene. An image enhancement module is employed to improve the image quality and extend the observation range. Subsequently, a triplet attention mechanism is introduced to the YOLOV5 model to improve the feature extraction ability. Moreover, the structure of the prediction head of YOLOV5 is optimized to capture small-sized objects. Ablation studies are conducted to analyze and validate the effective performance of each module. Moreover, performance evaluation results demonstrate that our proposed marine organism detection model is superior to the state-of-the-art models in both accuracy and speed. Furthermore, the proposed model is deployed on an embedded device and its processing time is less than 1 s. These results show that the proposed model has the potential for real-time observation by mobile platforms or undersea equipment. Full article
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22 pages, 5251 KiB  
Article
Research on an Extensible Monitoring System of a Seafloor Observatory Network in Laizhou Bay
by Jie Chen, Hailin Liu, Bin Lv, Chao Liu, Xiaonan Zhang, Hui Li, Lin Cao and Junhe Wan
J. Mar. Sci. Eng. 2022, 10(8), 1051; https://doi.org/10.3390/jmse10081051 - 30 Jul 2022
Cited by 2 | Viewed by 1721
Abstract
An extensible remote monitoring system for a seafloor observatory network in Laizhou Bay was established to realize long-term, continuous and on-line monitoring for a marine ranching environment. This paper deals with data communication, device management and data quality control. A control model is [...] Read more.
An extensible remote monitoring system for a seafloor observatory network in Laizhou Bay was established to realize long-term, continuous and on-line monitoring for a marine ranching environment. This paper deals with data communication, device management and data quality control. A control model is introduced that is structured into four layers, enabling bidirectional information flow. Based on the control model, the standardized communication protocol and device object model-oriented dynamic management method are designed as plug-and-play, for data processing and control of a large number of devices. An improved data quality control method is proposed to reduce the data error rate. The monitoring system was developed based on socket network programming, MySQL database technologies and modular ideas. The seafloor observatory network was successfully deployed in Laizhou Bay marine ranching. The experimental results demonstrate that the monitoring system obtains better performance. The proposed algorithms can also be used in many other similar systems with adaptive requirements. Full article
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23 pages, 28765 KiB  
Article
On the Adaptation of an AUV into a Dedicated Platform for Close Range Imaging Survey Missions
by Yevgeni Gutnik, Aviad Avni, Tali Treibitz and Morel Groper
J. Mar. Sci. Eng. 2022, 10(7), 974; https://doi.org/10.3390/jmse10070974 - 15 Jul 2022
Cited by 11 | Viewed by 3728
Abstract
This study presents the redesign of an existing autonomous underwater vehicle (AUV) with limited maneuverability, transforming it into a platform optimized for autonomous, near-seabed visual imaging missions. This work describes the enhancement of the AUV’s maneuverability through the addition of thrusters, the leveraging [...] Read more.
This study presents the redesign of an existing autonomous underwater vehicle (AUV) with limited maneuverability, transforming it into a platform optimized for autonomous, near-seabed visual imaging missions. This work describes the enhancement of the AUV’s maneuverability through the addition of thrusters, the leveraging of a state-of-the-art thrust allocation algorithm, and the development of both a path-following controller and a dedicated imaging system. The performance of the optimized platform is demonstrated in a simulation and in actual real sea visual survey missions. Full article
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22 pages, 6174 KiB  
Article
Improved RRT Algorithm for AUV Target Search in Unknown 3D Environment
by Juan Li, Chengyue Li, Tao Chen and Yun Zhang
J. Mar. Sci. Eng. 2022, 10(6), 826; https://doi.org/10.3390/jmse10060826 - 17 Jun 2022
Cited by 17 | Viewed by 2667
Abstract
Due to the complexity of the marine environment, underwater target search and interception is one of the biggest problems faced by an autonomous underwater vehicle (AUV). At present, there is quite a lot of research in terms of the two-dimensional environment. This paper [...] Read more.
Due to the complexity of the marine environment, underwater target search and interception is one of the biggest problems faced by an autonomous underwater vehicle (AUV). At present, there is quite a lot of research in terms of the two-dimensional environment. This paper proposes an improved rapidly exploring random trees (RRT) algorithm to solve the problem of target search and interception in an unknown three-dimensional (3D) environment. The RRT algorithm is combined with rolling planning and node screening to realize path planning in an unknown environment, and then the improved RRT algorithm is applied to the search and interception process in a 3D environment. Combined with the search decision function and the three-point numerical differential prediction method, the RRT algorithm can search for and effectively intercept the target. Numerical simulations in various situations show the superior performance, in terms of time and accuracy, of the proposed approach. Full article
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23 pages, 12806 KiB  
Article
Model-Free High-Order Sliding Mode Controller for Station-Keeping of an Autonomous Underwater Vehicle in Manipulation Task: Simulations and Experimental Validation
by Josué González-García, Alfonso Gómez-Espinosa, Luis Govinda García-Valdovinos, Tomás Salgado-Jiménez, Enrique Cuan-Urquizo and Jesús Arturo Escobedo Cabello
Sensors 2022, 22(12), 4347; https://doi.org/10.3390/s22124347 - 8 Jun 2022
Cited by 4 | Viewed by 2252
Abstract
The use of autonomous underwater vehicles (AUVs) has expanded in recent years to include inspection, maintenance, and repair missions. For these tasks, the vehicle must maintain its position while inspections or manipulations are performed. Some station-keeping controllers for AUVs can be found in [...] Read more.
The use of autonomous underwater vehicles (AUVs) has expanded in recent years to include inspection, maintenance, and repair missions. For these tasks, the vehicle must maintain its position while inspections or manipulations are performed. Some station-keeping controllers for AUVs can be found in the literature that exhibits robust performance against external disturbances. However, they are either model-based or require an observer to deal with the disturbances. Moreover, most of them have been evaluated only by numerical simulations. In this paper, the feasibility of a model-free high-order sliding mode controller for the station-keeping problem is validated. The proposed controller was evaluated through numerical simulations and experiments in a semi-Olympic swimming pool, introducing external disturbances that remained unknown to the controller. Results have shown robust performance in terms of the root mean square error (RMSE) of the vehicle position. The simulation resulted in the outstanding station-keeping of the BlueROV2 vehicle, as the tracking errors were kept to zero throughout the simulation, even in the presence of strong ocean currents. The experimental results demonstrated the robustness of the controller, which was able to maintain the RMSE in the range of 1–4 cm for the depth of the vehicle, outperforming related work, even when the disturbance was large enough to produce thruster saturation. Full article
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14 pages, 4882 KiB  
Article
An Underwater Glider with Muscle—Actuated Buoyancy Control and Caudal Fin Turning
by Biao Wang, Yishan Chen, Yige Wu, Yi Lin, Sijie Peng, Xiaohan Liu, Shijian Wu, Sicong Liu, Juan Yi, Panagiotis Polygerinos and Zheng Wang
Machines 2022, 10(5), 381; https://doi.org/10.3390/machines10050381 - 16 May 2022
Cited by 4 | Viewed by 3088
Abstract
Underwater robotic gliders exploit gravity and buoyancy for long-distance cruising with ultra-low energy consumptions, making them ideal for open ocean surveying operations. However, the gliding-based motion generation principle also prevents their maneuverability, limiting their use in the short distances that are usually encountered [...] Read more.
Underwater robotic gliders exploit gravity and buoyancy for long-distance cruising with ultra-low energy consumptions, making them ideal for open ocean surveying operations. However, the gliding-based motion generation principle also prevents their maneuverability, limiting their use in the short distances that are usually encountered in harbors or coastal scenarios. In this work, an innovative underwater glider robot is developed, enabling maneuverability through the introduction of an efficiently actuated caudal fin with bidirectional turning capabilities. In addition, modular actuator units, based on soft actuated materials, are integrated to control pitch angle by dynamically shifting the center of mass from the center of buoyancy. As a result, the high energy efficiency feature of the gliders is maintained, while high maneuverability is also achieved. The design concept, modeling of key components, and framework for control are presented, with the prototyped glider tested in a series of bench and field trials for validation of its motion performance. Full article
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22 pages, 23208 KiB  
Article
Marine Adaptive Sampling Scheme Design for Mobile Platforms under Different Scenarios
by Yuxin Zhao, Hengde Zhao, Yanlong Liu and Xiong Deng
J. Mar. Sci. Eng. 2022, 10(5), 664; https://doi.org/10.3390/jmse10050664 - 13 May 2022
Cited by 2 | Viewed by 1810
Abstract
Marine adaptive sampling is a technique that makes full use of limited observation resources by selecting the optimal positions. Recently, the design of an adaptive sampling scheme based on a mobile platform has become a research hotspot. However, adaptive sampling system involves multiple [...] Read more.
Marine adaptive sampling is a technique that makes full use of limited observation resources by selecting the optimal positions. Recently, the design of an adaptive sampling scheme based on a mobile platform has become a research hotspot. However, adaptive sampling system involves multiple subsystems, and the attributes as well as tasks are always different, which may lead to different sampling scenarios. A great deal of research has been conducted for specific situations, especially with fixed starting and ending points. However, systematic design and simulation experiments under various circumstances are still lacking. How to design the adaptive observation system, so as to cope with the observation task under different scenarios, is still a problem worth studying. Aiming to solve this problem, we designed a systematic scheme design process. The process includes setting up and verifying the background field, adopting the hierarchical optimization framework to adapt to different circumstances, and variable adjustments for twin frames. The needs covered in this paper include not having a fixed starting point and ending point, only having a fixed starting point, having a fixed starting point and ending point, increasing sampling coverage, and simple obstacle avoidance. Finally, the relevant conclusions are applied to the multi-platform simultaneous observation scenario. It provides a systematic flow pattern for designing adaptive sampling scheme of mobile platforms. Full article
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13 pages, 1565 KiB  
Article
Validation and Improvement of COCTS/HY-1C Sea Surface Temperature Products
by Feizhou Zhang, Yulin Zhang, Zihan Zhang and Jing Ding
Sensors 2022, 22(10), 3726; https://doi.org/10.3390/s22103726 - 13 May 2022
Viewed by 1495
Abstract
In oceanographic study, satellite-based sea surface temperature (SST) retrieval has always been the focus of researchers. This paper investigates several multi-channel SST retrieval algorithms for the thermal infrared band, and evaluates the accuracy of the COCTS/HY-1C SST products. NEAR-GOOS in situ SST data [...] Read more.
In oceanographic study, satellite-based sea surface temperature (SST) retrieval has always been the focus of researchers. This paper investigates several multi-channel SST retrieval algorithms for the thermal infrared band, and evaluates the accuracy of the COCTS/HY-1C SST products. NEAR-GOOS in situ SST data are utilized for validation and improvement, and a three-step matching procedure including geographic location screening, cloud masking, and homogeneity check is conducted to match in situ SST data with satellite SST data. Two improvement schemes, including nonlinear regression and regularization iteration, are proposed to improve the accuracy of the COCTS/HY-1C SST products and the typical application scenarios and the algorithm characteristics of these two schemes are discussed. The standard deviation of residual between retrieved SST and measured SST for these two data improvement algorithms, which are considered as the main indexes for assessment, result in an improvement of 13.245% and 14.096%, respectively. In addition, the generalization ability of the SST models under two data improvement methods is quantitatively compared, and the factors affecting the model accuracy are also carefully evaluated, including the in situ data acquisition method and measurement time (day/night). Finally, future works about SST retrieval with COCTS/HY-1C satellite data are summarized. Full article
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21 pages, 5112 KiB  
Article
Research on Trajectory Tracking Control of Underwater Vehicle Manipulator System Based on Model-Free Adaptive Control Method
by Gang Xue, Yanjun Liu, Zhenjie Shi, Lei Guo and Zhitong Li
J. Mar. Sci. Eng. 2022, 10(5), 652; https://doi.org/10.3390/jmse10050652 - 11 May 2022
Cited by 13 | Viewed by 2345
Abstract
In order to improve the trajectory tracking accuracy of an Underwater Vehicle Manipulator System (UVMS) under uncertain disturbance conditions of ocean current, a Model-free Adaptive Control (MFAC) method was used. Combined with Radial Basis Function Neural Networks (RBFNN), the RBFNN-MFAC method is proposed [...] Read more.
In order to improve the trajectory tracking accuracy of an Underwater Vehicle Manipulator System (UVMS) under uncertain disturbance conditions of ocean current, a Model-free Adaptive Control (MFAC) method was used. Combined with Radial Basis Function Neural Networks (RBFNN), the RBFNN-MFAC method is proposed to improve the performance of the controller. A hydrodynamic model of UVMS was defined in the commercial software, Fluent, to calculate hydrodynamics disturbance, and the mechanism of the dynamic model of UVMS was defined in the commercial software, Adams, to simulate the motion of UVMS. The trajectory tracking performance with various control schemes, including PID (Proportional Integral Derivative), MFAC and RBFNN-MFAC, were analyzed with the Adams and Simulink joint simulation model. The results show that the position tracking accuracy and the speed tracking accuracy with the MFAC control scheme were 68.1% and 81.0% better, respectively, than those with PID control scheme. The position tracking accuracy and the speed tracking accuracy with the RBFNN-MFAC control scheme were 66.3% and 43.1% better, respectively, than those with the MFAC control scheme. The MFAC control scheme and the RBFNN-MFAC control scheme proposed in this paper exhibit good trajectory tracking performance without the precise dynamic model of UVMS, which is of great importance to applications in engineering. Full article
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23 pages, 5432 KiB  
Article
Distributed Dynamic Predictive Control for Multi-AUV Target Searching and Hunting in Unknown Environments
by Juan Li, Chengyue Li and Honghan Zhang
Machines 2022, 10(5), 366; https://doi.org/10.3390/machines10050366 - 11 May 2022
Cited by 3 | Viewed by 1935
Abstract
The research and development of the ocean has been gaining in popularity in recent years, and the problem of target searching and hunting in the unknown marine environment has been a pressing problem. To solve this problem, a distributed dynamic predictive control (DDPC) [...] Read more.
The research and development of the ocean has been gaining in popularity in recent years, and the problem of target searching and hunting in the unknown marine environment has been a pressing problem. To solve this problem, a distributed dynamic predictive control (DDPC) algorithm based on the idea of predictive control is proposed. The task-environment region information and the input of the AUV state update are obtained by predicting the state of multi-AUV systems and making online task optimization decisions and then locking the search area for the following moment. Once a moving target is found in the search process, the AUV conducts a distributed hunt based on the theory of potential points, which solves the problem of the reasonable distribution of potential points during the hunting process and realizes the formation of hunting rapidly. Compared with other methods, the simulation results show that the algorithm exhibits high efficiency and adaptability. Full article
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29 pages, 43520 KiB  
Article
Intelligent Ship Collision Avoidance Algorithm Based on DDQN with Prioritized Experience Replay under COLREGs
by Pengyu Zhai, Yingjun Zhang and Wang Shaobo
J. Mar. Sci. Eng. 2022, 10(5), 585; https://doi.org/10.3390/jmse10050585 - 26 Apr 2022
Cited by 34 | Viewed by 4799
Abstract
Ship collisions often result in huge losses of life, cargo and ships, as well as serious pollution of the water environment. Meanwhile, it is estimated that between 75% and 86% of maritime accidents are related to human factors. Thus, it is necessary to [...] Read more.
Ship collisions often result in huge losses of life, cargo and ships, as well as serious pollution of the water environment. Meanwhile, it is estimated that between 75% and 86% of maritime accidents are related to human factors. Thus, it is necessary to enhance the intelligence of ships to partially or fully replace the traditional piloting mode and eventually achieve autonomous collision avoidance to reduce the influence of human factors. In this paper, we propose a multi-ship automatic collision avoidance method based on a double deep Q network (DDQN) with prioritized experience replay. Firstly, we vectorize the predicted hazardous areas as the observation states of the agent so that similar ship encounter scenarios can be clustered and the input dimension of the neural network can be fixed. The reward function is designed based on the International Regulations for Preventing Collision at Sea (COLREGs) and human experience. Different from the architecture of previous collision avoidance methods based on deep reinforcement learning (DRL), in this paper, the interaction between the agent and the environment occurs only in the collision avoidance decision-making phase, which greatly reduces the number of state transitions in the Markov decision process (MDP). The prioritized experience replay method is also used to make the model converge more quickly. Finally, 19 single-vessel collision avoidance scenarios were constructed based on the encounter situations classified by the COLREGs, which were arranged and combined as the training set for the agent. The effectiveness of the proposed method in close-quarters situation was verified using the Imazu problem. The simulation results show that the method can achieve multi-ship collision avoidance in crowded waters, and the decisions generated by this method conform to the COLREGs and are close to the level of human ship handling. Full article
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18 pages, 2497 KiB  
Article
Source Localisation Using Wavefield Correlation-Enhanced Particle Swarm Optimisation
by George Rossides, Alan Hunter and Benjamin Metcalfe
Robotics 2022, 11(2), 52; https://doi.org/10.3390/robotics11020052 - 18 Apr 2022
Viewed by 2580
Abstract
Particle swarm optimisation (PSO) is a swarm intelligence algorithm used for controlling robotic swarms in applications such as source localisation. However, conventional PSO algorithms consider only the intensity of the received signal. Wavefield signals, such as propagating underwater acoustic waves, permit the measurement [...] Read more.
Particle swarm optimisation (PSO) is a swarm intelligence algorithm used for controlling robotic swarms in applications such as source localisation. However, conventional PSO algorithms consider only the intensity of the received signal. Wavefield signals, such as propagating underwater acoustic waves, permit the measurement of higher order statistics that can be used to provide additional information about the location of the source and thus improve overall swarm performance. Wavefield correlation techniques that make use of such information are already used in multi-element hydrophone array systems for the localisation of underwater marine sources. Additionally, the simplest model of a multi-element array (a two-element array) is characterised by operational simplicity and low-cost, which matches the ethos of robotic swarms. Thus, in this paper, three novel approaches are introduced that enable PSO to consider the higher order statistics available in wavefield measurements. In simulations, they are shown to outperform the standard intensity-based PSO in terms of robustness to low signal-to-noise ratio (SNR) and convergence speed. The best performing approach, cross-correlation bearing PSO (XB-PSO), is capable of converging to the source from as low as −5 dB initial SNR. The original PSO algorithm only manages to converge at 10 dB and at this SNR, XB-PSO converges 4 times faster. Full article
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