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39 pages, 13694 KiB

Open AccessArticle

Artificial Intelligence Search Strategies for Autonomous Underwater Vehicles Applied for Submarine Groundwater Discharge Site Investigation

by Christoph Tholen, Tarek A. El-Mihoub, Lars Nolle and Oliver Zielinski

J. Mar. Sci. Eng. 2022, 10(1), 7; https://0-doi-org.brum.beds.ac.uk/10.3390/jmse10010007 - 22 Dec 2021

Cited by 7 | Viewed by 3417

Abstract

In this study, a set of different search strategies for locating submarine groundwater discharge (SGD) are investigated. This set includes pre-defined path planning (PPP), adapted random walk (RW), particle swarm optimisation (PSO), inertia Levy-flight (ILF), self-organising-migration-algorithm (SOMA), and bumblebee search algorithm (BB). The [...] Read more.

In this study, a set of different search strategies for locating submarine groundwater discharge (SGD) are investigated. This set includes pre-defined path planning (PPP), adapted random walk (RW), particle swarm optimisation (PSO), inertia Levy-flight (ILF), self-organising-migration-algorithm (SOMA), and bumblebee search algorithm (BB). The influences of self-localisation and communication errors and limited travel distance of the autonomous underwater vehicles (AUVs) on the performance of the proposed algorithms are investigated. This study shows that the proposed search strategies could not outperform the classic search heuristic based on full coverage path planning if all AUVs followed the same search strategy. In this study, the influence of self-localisation and communication errors was investigated. The simulations showed that, based on the median error of the search runs, the performance of SOMA was in the same order of magnitude regardless the strength of the localisation error. Furthermore, it was shown that the performance of BB was highly affected by increasing localisation errors. From the simulations, it was revealed that all the algorithms, except for PSO and SOMA, were unaffected by disturbed communications. Here, the best performance was shown by PPP, followed by BB, SOMA, ILF, PSO, and RW. Furthermore, the influence of the limited travel distances of the AUVs on the search performance was evaluated. It was shown that all the algorithms, except for PSO, were affected by the shorter maximum travel distances of the AUVs. The performance of PPP increased with increasing maximum travel distances. However, for maximum travel distances > 1800 m the median error appeared constant. The effect of shorter travel distances on SOMA was smaller than on PPP. For maximum travel distances < 1200 m, SOMA outperformed all other strategies. In addition, it can be observed that only BB showed better performances for shorter travel distances than for longer ones. On the other hand, with different search strategies for each AUV, the search performance of the whole swarm can be improved by incorporating population-based search strategies such as PSO and SOMA within the PPP scheme. The best performance was achieved for the combination of two AUVs following PPP, while the third AUV utilised PSO. The best fitness of this combination was 15.9. This fitness was 26.4% better than the performance of PPP, which was 20.4 on average. In addition, a novel mechanism for dynamically selecting a search strategy for an AUV is proposed. This mechanism is based on fuzzy logic. This dynamic approach is able to perform at least as well as PPP and SOMA for different travel distances of AUVs. However, due to the better adaptation to the current situation, the overall performance, calculated based on the fitness achieved for different maximum travel distances, the proposed dynamic search strategy selection performed 32.8% better than PPP and 34.0% better than SOMA. Full article

(This article belongs to the Special Issue Machine Learning and Remote Sensing in Ocean Science and Engineering)

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23 pages, 55037 KiB

Open AccessArticle

Path Planning for Underwater Information Gathering Based on Genetic Algorithms and Data Stochastic Models

by Matteo Bresciani, Francesco Ruscio, Simone Tani, Giovanni Peralta, Andrea Timperi, Eric Guerrero-Font, Francisco Bonin-Font, Andrea Caiti and Riccardo Costanzi

J. Mar. Sci. Eng. 2021, 9(11), 1183; https://0-doi-org.brum.beds.ac.uk/10.3390/jmse9111183 - 27 Oct 2021

Cited by 14 | Viewed by 2665

Abstract

Recent technological developments have paved the way to the employment of Autonomous Underwater Vehicles (AUVs) for monitoring and exploration activities of marine environments. Traditionally, in information gathering scenarios for monitoring purposes, AUVs follow predefined paths that are not efficient in terms of information [...] Read more.

Recent technological developments have paved the way to the employment of Autonomous Underwater Vehicles (AUVs) for monitoring and exploration activities of marine environments. Traditionally, in information gathering scenarios for monitoring purposes, AUVs follow predefined paths that are not efficient in terms of information content and energy consumption. Informative Path Planning (IPP) represents a valid alternative, defining the path that maximises the gathered information. This work proposes a Genetic Path Planner (GPP), which consists in an IPP strategy based on a Genetic Algorithm, with the aim of generating a path that simultaneously maximises the information gathered and the coverage of the inspected area. The proposed approach has been tested offline for monitoring and inspection applications of Posidonia Oceanica (PO) in three different geographical areas. The a priori knowledge about the presence of PO, in probabilistic terms, has been modelled utilising a Gaussian Process (GP), trained on real marine data. The GP estimate has then been exploited to retrieve an information content of each position in the areas of interest. A comparison with other two IPP approaches has been carried out to assess the performance of the proposed algorithm. Full article

(This article belongs to the Special Issue Machine Learning and Remote Sensing in Ocean Science and Engineering)

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17 pages, 10684 KiB

Open AccessEditor’s ChoiceArticle

A Novel Cargo Ship Detection and Directional Discrimination Method for Remote Sensing Image Based on Lightweight Network

by Pan Wang, Jianzhong Liu, Yinbao Zhang, Zhiyang Zhi, Zhijian Cai and Nannan Song

J. Mar. Sci. Eng. 2021, 9(9), 932; https://0-doi-org.brum.beds.ac.uk/10.3390/jmse9090932 - 28 Aug 2021

Cited by 2 | Viewed by 2483

Abstract

Recently, cargo ship detection in remote sensing images based on deep learning is of great significance for cargo ship monitoring. However, the existing detection network is not only unable to realize autonomous operation on spaceborne platforms due to the limitation of computing and [...] Read more.

Recently, cargo ship detection in remote sensing images based on deep learning is of great significance for cargo ship monitoring. However, the existing detection network is not only unable to realize autonomous operation on spaceborne platforms due to the limitation of computing and storage, but the detection result also lacks the directional information of the cargo ship. In order to address the above problems, we propose a novel cargo ship detection and directional discrimination method for remote sensing images based on a lightweight network. Specifically, we design an efficient and lightweight feature extraction network called the one-shot aggregation and depthwise separable network (OSADSNet), which is inspired by one-shot feature aggregation modules and depthwise separable convolutions. Additionally, we combine the RPN with the K-Mean++ algorithm to obtain the K-RPN, which can produce a more suitable region proposal for cargo ship detection. Furthermore, without introducing extra parameters, the directional discrimination of the cargo ship is transformed into a classification task, and the directional discrimination is completed when the detection task is completed. Experiments on a self-built remote sensing image cargo ship dataset indicate that our model can provide relatively accurate and fast detection for cargo ships (mAP of 91.96% and prediction time of 46 ms per image) and discriminate the directions (north, east, south, and west) of cargo ships, with fewer parameters (model size of 110 MB), which is more suitable for autonomous operation on spaceborne platforms. Therefore, the proposed method can meet the needs of cargo ship detection and directional discrimination in remote sensing images on spaceborne platforms. Full article

(This article belongs to the Special Issue Machine Learning and Remote Sensing in Ocean Science and Engineering)

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34 pages, 14476 KiB

Open AccessArticle

How Good Is the STW Sensor? An Account from a Larger Shipping Company

by Angelos Ikonomakis, Ulrik Dam Nielsen, Klaus Kähler Holst, Jesper Dietz and Roberto Galeazzi

J. Mar. Sci. Eng. 2021, 9(5), 465; https://0-doi-org.brum.beds.ac.uk/10.3390/jmse9050465 - 25 Apr 2021

Cited by 9 | Viewed by 3668

Abstract

This paper examines the statistical properties and the quality of the speed through water (STW) measurement based on data extracted from almost 200 container ships of Maersk Line’s fleet for 3 years of operation. The analysis uses high-frequency sensor data along with additional [...] Read more.

This paper examines the statistical properties and the quality of the speed through water (STW) measurement based on data extracted from almost 200 container ships of Maersk Line’s fleet for 3 years of operation. The analysis uses high-frequency sensor data along with additional data sources derived from external providers. The interest of the study has its background in the accuracy of STW measurement as the most important parameter in the assessment of a ship’s performance analysis. The paper contains a thorough analysis of the measurements assumed to be related with the STW error, along with a descriptive decomposition of the main variables by sea region including sea state, vessel class, vessel IMO number and manufacturer of the speed-log installed in each ship. The paper suggests a semi-empirical method using a threshold to identify potential error in a ship’s STW measurement. The study revealed that the sea region is the most influential factor for the STW accuracy and that

26 %

of the ships of the dataset’s fleet warrant further investigation. Full article

(This article belongs to the Special Issue Machine Learning and Remote Sensing in Ocean Science and Engineering)

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16 pages, 4848 KiB

Open AccessArticle

Underwater Image Enhancement Based on Local Contrast Correction and Multi-Scale Fusion

by Farong Gao, Kai Wang, Zhangyi Yang, Yejian Wang and Qizhong Zhang

J. Mar. Sci. Eng. 2021, 9(2), 225; https://0-doi-org.brum.beds.ac.uk/10.3390/jmse9020225 - 19 Feb 2021

Cited by 29 | Viewed by 4574

Abstract

In this study, an underwater image enhancement method based on local contrast correction (LCC) and multi-scale fusion is proposed to resolve low contrast and color distortion of underwater images. First, the original image is compensated using the red channel, and the compensated image [...] Read more.

In this study, an underwater image enhancement method based on local contrast correction (LCC) and multi-scale fusion is proposed to resolve low contrast and color distortion of underwater images. First, the original image is compensated using the red channel, and the compensated image is processed with a white balance. Second, LCC and image sharpening are carried out to generate two different image versions. Finally, the local contrast corrected images are fused with sharpened images by the multi-scale fusion method. The results show that the proposed method can be applied to water degradation images in different environments without resorting to an image formation model. It can effectively solve color distortion, low contrast, and unobvious details of underwater images. Full article

(This article belongs to the Special Issue Machine Learning and Remote Sensing in Ocean Science and Engineering)

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23 pages, 5644 KiB

Open AccessArticle

Path Planning of Coastal Ships Based on Optimized DQN Reward Function

by Siyu Guo, Xiuguo Zhang, Yiquan Du, Yisong Zheng and Zhiying Cao

J. Mar. Sci. Eng. 2021, 9(2), 210; https://0-doi-org.brum.beds.ac.uk/10.3390/jmse9020210 - 18 Feb 2021

Cited by 74 | Viewed by 6965

Abstract

Path planning is a key issue in the field of coastal ships, and it is also the core foundation of ship intelligent development. In order to better realize the ship path planning in the process of navigation, this paper proposes a coastal ship [...] Read more.

Path planning is a key issue in the field of coastal ships, and it is also the core foundation of ship intelligent development. In order to better realize the ship path planning in the process of navigation, this paper proposes a coastal ship path planning model based on the optimized deep Q network (DQN) algorithm. The model is mainly composed of environment status information and the DQN algorithm. The environment status information provides training space for the DQN algorithm and is quantified according to the actual navigation environment and international rules for collision avoidance at sea. The DQN algorithm mainly includes four components which are ship state space, action space, action exploration strategy and reward function. The traditional reward function of DQN may lead to the low learning efficiency and convergence speed of the model. This paper optimizes the traditional reward function from three aspects: (a) the potential energy reward of the target point to the ship is set; (b) the reward area is added near the target point; and (c) the danger area is added near the obstacle. Through the above optimized method, the ship can avoid obstacles to reach the target point faster, and the convergence speed of the model is accelerated. The traditional DQN algorithm, A* algorithm, BUG2 algorithm and artificial potential field (APF) algorithm are selected for experimental comparison, and the experimental data are analyzed from the path length, planning time, number of path corners. The experimental results show that the optimized DQN algorithm has better stability and convergence, and greatly reduces the calculation time. It can plan the optimal path in line with the actual navigation rules, and improve the safety, economy and autonomous decision-making ability of ship navigation. Full article

(This article belongs to the Special Issue Machine Learning and Remote Sensing in Ocean Science and Engineering)

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12 pages, 5380 KiB

Open AccessArticle

A Study on Enhancement of Fish Recognition Using Cumulative Mean of YOLO Network in Underwater Video Images

by Jin-Hyun Park and Changgu Kang

J. Mar. Sci. Eng. 2020, 8(11), 952; https://0-doi-org.brum.beds.ac.uk/10.3390/jmse8110952 - 22 Nov 2020

Cited by 16 | Viewed by 3279

Abstract

In the underwater environment, in order to preserve rare and endangered objects or to eliminate the exotic invasive species that can destroy the ecosystems, it is essential to classify objects and estimate their number. It is very difficult to classify objects and estimate [...] Read more.

In the underwater environment, in order to preserve rare and endangered objects or to eliminate the exotic invasive species that can destroy the ecosystems, it is essential to classify objects and estimate their number. It is very difficult to classify objects and estimate their number. While YOLO shows excellent performance in object recognition, it recognizes objects by processing the images of each frame independently of each other. By accumulating the object classification results from the past frames to the current frame, we propose a method to accurately classify objects, and count their number in sequential video images. This has a high classification probability of 93.94% and 97.06% in the test videos of Bluegill and Largemouth bass, respectively. The proposed method shows very good classification performance in video images taken of the underwater environment. Full article

(This article belongs to the Special Issue Machine Learning and Remote Sensing in Ocean Science and Engineering)

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Review

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17 pages, 294 KiB

Open AccessReview

Game Theory for Unmanned Vehicle Path Planning in the Marine Domain: State of the Art and New Possibilities

by Marco Cococcioni, Lorenzo Fiaschi and Pierre F. J. Lermusiaux

J. Mar. Sci. Eng. 2021, 9(11), 1175; https://0-doi-org.brum.beds.ac.uk/10.3390/jmse9111175 - 26 Oct 2021

Cited by 4 | Viewed by 2405

Abstract

Thanks to the advent of new technologies and higher real-time computational capabilities, the use of unmanned vehicles in the marine domain has received a significant boost in the last decade. Ocean and seabed sampling, missions in dangerous areas, and civilian security are only [...] Read more.

Thanks to the advent of new technologies and higher real-time computational capabilities, the use of unmanned vehicles in the marine domain has received a significant boost in the last decade. Ocean and seabed sampling, missions in dangerous areas, and civilian security are only a few of the large number of applications which currently benefit from unmanned vehicles. One of the most actively studied topic is their full autonomy; i.e., the design of marine vehicles capable of pursuing a task while reacting to the changes of the environment without the intervention of humans, not even remotely. Environmental dynamicity may consist of variations of currents, the presence of unknown obstacles, and attacks from adversaries (e.g., pirates). To achieve autonomy in such highly dynamic uncertain conditions, many types of autonomous path planning problems need to be solved. There has thus been a commensurate number of approaches and methods to optimize this kind of path planning. This work focuses on game-theoretic approaches and provides a wide overview of the current state of the art, along with future directions. Full article

(This article belongs to the Special Issue Machine Learning and Remote Sensing in Ocean Science and Engineering)

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