Journal of Marine Science and Engineering

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3 pages, 165 KiB

Open AccessEditorial

Unmanned Marine Vehicles

by Gabriele Bruzzone, Roberta Ferretti and Angelo Odetti

J. Mar. Sci. Eng. 2021, 9(3), 257; https://doi.org/10.3390/jmse9030257 - 01 Mar 2021

Cited by 1 | Viewed by 1715

Abstract

Water covers nearly 70% of the Earth’s surface, and throughout history, oceans, seas, lakes, rivers, etc [...] Full article

(This article belongs to the Special Issue Unmanned Marine Vehicles)

Research

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17 pages, 26462 KiB

Open AccessArticle

Ship-Collision Avoidance Decision-Making Learning of Unmanned Surface Vehicles with Automatic Identification System Data Based on Encoder—Decoder Automatic-Response Neural Networks

by Miao Gao and Guo-You Shi

J. Mar. Sci. Eng. 2020, 8(10), 754; https://doi.org/10.3390/jmse8100754 - 27 Sep 2020

Cited by 9 | Viewed by 2925

Abstract

Intelligent unmanned surface vehicle (USV) collision avoidance is a complex inference problem based on current navigation status. This requires simultaneous processing of the input sequences and generation of the response sequences. The automatic identification system (AIS) encounter data mainly include the time-series data [...] Read more.

Intelligent unmanned surface vehicle (USV) collision avoidance is a complex inference problem based on current navigation status. This requires simultaneous processing of the input sequences and generation of the response sequences. The automatic identification system (AIS) encounter data mainly include the time-series data of two AIS sets, which exhibit a one-to-one mapping relation. Herein, an encoder–decoder automatic-response neural network is designed and implemented based on the sequence-to-sequence (Seq2Seq) structure to simultaneously process the two AIS encounter trajectory sequences. Furthermore, this model is combined with the bidirectional long short-term memory recurrent neural networks (Bi-LSTM RNN) to obtain a network framework for processing the time-series data to obtain ship-collision avoidance decisions based on big data. The encoder–decoder neural networks were trained based on the AIS data obtained in 2018 from Zhoushan Port to achieve ship collision avoidance decision-making learning. The results indicated that the encoder–decoder neural networks can be used to effectively formulate the sequence of the collision avoidance decision of the USV. Thus, this study significantly contributes to the increased efficiency and safety of maritime transportation. The proposed method can potentially be applied to the USV technology and intelligent collision-avoidance systems. Full article

(This article belongs to the Special Issue Unmanned Marine Vehicles)

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32 pages, 1175 KiB

Open AccessFeature PaperArticle

Wireless Remote Control for Underwater Vehicles

by Filippo Campagnaro, Alberto Signori and Michele Zorzi

J. Mar. Sci. Eng. 2020, 8(10), 736; https://doi.org/10.3390/jmse8100736 - 24 Sep 2020

Cited by 24 | Viewed by 8152

Abstract

Nowadays, the increasing availability of commercial off-the-shelf underwater acoustic and non-acoustic (e.g., optical and electromagnetic) modems that can be employed for both short-range broadband and long-range low-rate communication, the increasing level of autonomy of underwater vehicles, and the refinement of their underwater navigation [...] Read more.

Nowadays, the increasing availability of commercial off-the-shelf underwater acoustic and non-acoustic (e.g., optical and electromagnetic) modems that can be employed for both short-range broadband and long-range low-rate communication, the increasing level of autonomy of underwater vehicles, and the refinement of their underwater navigation systems pave the way for several new applications, such as data muling from underwater sensor networks and the transmission of real-time video streams underwater. In addition, these new developments inspired many companies to start designing hybrid wireless-driven underwater vehicles specifically tailored for off-shore operations and that are able to behave either as remotely operated vehicles (ROVs) or as autonomous underwater vehicles (AUVs), depending on both the type of mission they are required to perform and the limitations imposed by underwater communication channels. In this paper, we evaluate the actual quality of service (QoS) achievable with an underwater wireless-piloted vehicle, addressing the realistic aspects found in the underwater domain, first reviewing the current state-of-the-art of communication technologies and then proposing the list of application streams needed for control of the underwater vehicle, grouping them in different working modes according to the level of autonomy required by the off-shore mission. The proposed system is finally evaluated by employing the DESERT Underwater simulation framework by specifically analyzing the QoS that can be provided to each application stream when using a multimodal underwater communication system specifically designed to support different traffic-based QoSs. Both the analysis and the results show that changes in the underwater environment have a strong impact on the range and on the stability of the communication link. Full article

(This article belongs to the Special Issue Unmanned Marine Vehicles)

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19 pages, 9450 KiB

Open AccessArticle

Deep Learning in Unmanned Surface Vehicles Collision-Avoidance Pattern Based on AIS Big Data with Double GRU-RNN

by Jia-hui Shi and Zheng-jiang Liu

J. Mar. Sci. Eng. 2020, 8(9), 682; https://doi.org/10.3390/jmse8090682 - 04 Sep 2020

Cited by 26 | Viewed by 3548

Abstract

There is a collection of a large amount of automatic identification system (AIS) data that contains ship encounter information, but mining the collision avoidance knowledge from AIS big data and carrying out effective machine learning is a difficult problem in current maritime field. [...] Read more.

There is a collection of a large amount of automatic identification system (AIS) data that contains ship encounter information, but mining the collision avoidance knowledge from AIS big data and carrying out effective machine learning is a difficult problem in current maritime field. Herein, first the Douglas–Peucker (DP) algorithm was used to preprocess the AIS data. Then, based on the ship domain the risk of collision was identified. Finally, a double-gated recurrent unit neural network (GRU-RNN) was constructed to learn unmanned surface vehicle (USV) collision avoidance decision from the extracted data of successful encounters of ships. The double GRU-RNN was trained on the 2015 Tianjin Port AIS dataset to realize the effective learning of ship encounter data. The results indicated that the double GRU-RNN could effectively learn the collision avoidance pattern hidden in AIS big data, and generate corresponding ship collision-avoidance decisions for different maritime navigation states. This study contributes significantly to the increased efficiency and safety of sea operations. The proposed method could be potentially applied to USV technology and intelligence collision avoidance. Full article

(This article belongs to the Special Issue Unmanned Marine Vehicles)

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20 pages, 2541 KiB

Open AccessArticle

ENDURUNS: An Integrated and Flexible Approach for Seabed Survey Through Autonomous Mobile Vehicles

by Simone Marini, Nikolla Gjeci, Shashank Govindaraj, Alexandru But, Benjamin Sportich, Ennio Ottaviani, Fausto Pedro García Márquez, Pedro Jose Bernalte Sanchez, Jonas Pedersen, Casper Vetke Clausen, Fantina Madricardo, Fedeirca Foglini, Federico Bonofiglio, Laura Barbieri, Massimiliano Antonini, Yeidy Sorani Montenegro Camacho, Peter Weiss, Kathrin Nowak, Makthoum Peer, Thibaud Gobert, Alessio Turetta, Elias Chatzidouros, Dongik Lee, Dimitris Zarras, Theodore Steriotis, Georgia Charalambopoulou, Thanos Yamas and Mayorkinos Papaelias Show full author list Hide full author list

J. Mar. Sci. Eng. 2020, 8(9), 633; https://doi.org/10.3390/jmse8090633 - 20 Aug 2020

Cited by 39 | Viewed by 5159

Abstract

The oceans cover more than two-thirds of the planet, representing the vastest part of natural resources. Nevertheless, only a fraction of the ocean depths has been explored. Within this context, this article presents the H2020 ENDURUNS project that describes a novel scientific and [...] Read more.

The oceans cover more than two-thirds of the planet, representing the vastest part of natural resources. Nevertheless, only a fraction of the ocean depths has been explored. Within this context, this article presents the H2020 ENDURUNS project that describes a novel scientific and technological approach for prolonged underwater autonomous operations of seabed survey activities, either in the deep ocean or in coastal areas. The proposed approach combines a hybrid Autonomous Underwater Vehicle capable of moving using either thrusters or as a sea glider, combined with an Unmanned Surface Vehicle equipped with satellite communication facilities for interaction with a land station. Both vehicles are equipped with energy packs that combine hydrogen fuel cells and Li-ion batteries to provide extended duration of the survey operations. The Unmanned Surface Vehicle employs photovoltaic panels to increase the autonomy of the vehicle. Since these missions generate a large amount of data, both vehicles are equipped with onboard Central Processing units capable of executing data analysis and compression algorithms for the semantic classification and transmission of the acquired data. Full article

(This article belongs to the Special Issue Unmanned Marine Vehicles)

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23 pages, 2496 KiB

Open AccessArticle

A Novel Double Layered Hybrid Multi-Robot Framework for Guidance and Navigation of Unmanned Surface Vehicles in a Practical Maritime Environment

by Yogang Singh, Marco Bibuli, Enrica Zereik, Sanjay Sharma, Asiya Khan and Robert Sutton

J. Mar. Sci. Eng. 2020, 8(9), 624; https://doi.org/10.3390/jmse8090624 - 19 Aug 2020

Cited by 39 | Viewed by 4238

Abstract

Formation control and cooperative motion planning are two major research areas currently being used in multi robot motion planning and coordination. The current study proposes a hybrid framework for guidance and navigation of swarm of unmanned surface vehicles (USVs) by combining the key [...] Read more.

Formation control and cooperative motion planning are two major research areas currently being used in multi robot motion planning and coordination. The current study proposes a hybrid framework for guidance and navigation of swarm of unmanned surface vehicles (USVs) by combining the key characteristics of formation control and cooperative motion planning. In this framework, two layers of offline planning and online planning are integrated and applied on a practical marine environment. In offline planning, an optimal path is generated from a constrained A* path planning approach, which is later smoothed using a spline. This optimal trajectory is fed as an input for the online planning where virtual target (VT) based multi-agent guidance framework is used to navigate the swarm of USVs. This VT approach combined with a potential theory based swarm aggregation technique provides a robust methodology of global and local collision avoidance based on known positions of the USVs. The combined approach is evaluated with the different number of USVs to understand the effectiveness of the approach from the perspective of practicality, safety and robustness. Full article

(This article belongs to the Special Issue Unmanned Marine Vehicles)

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19 pages, 2746 KiB

Open AccessArticle

Using Autonomous Underwater Vehicles for Diver Tracking and Navigation Aiding

by Đula Nađ, Filip Mandić and Nikola Mišković

J. Mar. Sci. Eng. 2020, 8(6), 413; https://doi.org/10.3390/jmse8060413 - 05 Jun 2020

Cited by 20 | Viewed by 3590

Abstract

SCUBA diving activities are classified as high-risk due to the dangerous environment, dependency on technical equipment that ensures life support, reduced underwater navigation and communication capabilities all of which compromise diver safety. While autonomous underwater vehicles (AUVs) have become irreplaceable tools for seabed [...] Read more.

SCUBA diving activities are classified as high-risk due to the dangerous environment, dependency on technical equipment that ensures life support, reduced underwater navigation and communication capabilities all of which compromise diver safety. While autonomous underwater vehicles (AUVs) have become irreplaceable tools for seabed exploration, monitoring, and mapping in various applications, they still lack the higher cognitive capabilities offered by a human diver. The research presented in this paper was carried out under the EU FP7 “CADDY—Cognitive Autonomous Diving Buddy”. It aims to take advantage of both human diver and AUV complementary traits by making their synergy a potential solution for mitigation of state of the art diving challenges. The AUV increases diver safety by constantly observing the diver, provides navigation aiding by directing the diver and offers assistance (e.g., lights, tool fetching, etc.). The control algorithms proposed in the paper provide a foundation for implementing these services. These algorithms use measurements from stereo-camera, sonar and ultra-short baseline acoustic localization to ensure the vehicle constantly follows and observes the diver. Additionally, the vehicle maintains a relative formation with the diver to allow observation from multiple viewpoints and to aid underwater navigation by pointing towards the next point of interest. Performance of the proposed algorithms is evaluated using results from pool experiments. Full article

(This article belongs to the Special Issue Unmanned Marine Vehicles)

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24 pages, 2506 KiB

Open AccessArticle

Modeling of Autonomous Underwater Vehicles with Multi-Propellers Based on Maximum Likelihood Method

by Feiyan Min, Guoliang Pan and Xuefeng Xu

J. Mar. Sci. Eng. 2020, 8(6), 407; https://doi.org/10.3390/jmse8060407 - 04 Jun 2020

Cited by 15 | Viewed by 3402

Abstract

The hydrodynamic characteristics of multi-propeller autonomous underwater vehicles (AUV) is usually complicated and it is difficult to obtain an accurate mathematical model. A modeling method based on CFD calculation and maximum likelihood identification algorithm is proposed for this problem. Firstly, rough hydrodynamic parameters [...] Read more.

The hydrodynamic characteristics of multi-propeller autonomous underwater vehicles (AUV) is usually complicated and it is difficult to obtain an accurate mathematical model. A modeling method based on CFD calculation and maximum likelihood identification algorithm is proposed for this problem. Firstly, rough hydrodynamic parameters of AUV hull are obtained by CFD calculation. Secondly, on the basis of rough parameters, a maximum likelihood identification algorithm is proposed to adjust the parameters and improve the model precision. Besides, the method to improve the convergence of identification algorithm is analyzed by considering the characteristics of AUV model structure. Finally, the identification algorithm and identification results were validated with experimental data. It was found that this method has good convergence and adaptability. In particular, the identification results of turning force and torque parameters are highly consistent in different identification experiments, which indicates that this method can well extract the maneuvering characteristics of AUVs, thus contributing to the controller design of AUVs. The research of this paper has potential application for the modeling and control of multi-propeller AUVs. Full article

(This article belongs to the Special Issue Unmanned Marine Vehicles)

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18 pages, 4645 KiB

Open AccessArticle

Path Following of a Water-Jetted USV Based on Maneuverability Tests

by Junmin Mou, Yangying He, Benren Zhang, Shixuan Li and Yong Xiong

J. Mar. Sci. Eng. 2020, 8(5), 354; https://doi.org/10.3390/jmse8050354 - 16 May 2020

Cited by 13 | Viewed by 3172

Abstract

Due to the high propulsive efficiency and better maneuverability under high speed, the water-jetted unmanned surface vehicle (USV) is widely studied and used. This paper presents complete maneuvering tests and control algorithm designed for a twin water-jetted USV model. Firstly, a wireless network [...] Read more.

Due to the high propulsive efficiency and better maneuverability under high speed, the water-jetted unmanned surface vehicle (USV) is widely studied and used. This paper presents complete maneuvering tests and control algorithm designed for a twin water-jetted USV model. Firstly, a wireless network control platform is established, and maneuvering tests, for instance, an inertia test, zig-zag test and turning test, are carried out to verify the maneuverability of the USV. In light of the complexity and uncertainty of ship sailing and ship handling, the Human Simulated Intelligent Control (HSIC) method is utilized to optimize the response time, accuracy and robustness of the controller. Finally, for the path following and track rectification part, a Line of Sight (LOS) algorithm is improved and proved practicable with triangle/square path tests. The proposed intelligent navigation algorithm specially designed for matching with the control methods, showing satisfactory improvements on the motion control and path following of the specific USV. Full article

(This article belongs to the Special Issue Unmanned Marine Vehicles)

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23 pages, 13554 KiB

Open AccessArticle

Dynamics Modeling and Motion Simulation of USV/UUV with Linked Underwater Cable

by Sung Min Hong, Kyoung Nam Ha and Joon-Young Kim

J. Mar. Sci. Eng. 2020, 8(5), 318; https://doi.org/10.3390/jmse8050318 - 30 Apr 2020

Cited by 20 | Viewed by 4623

Abstract

This paper describes a study on the dynamic modeling and the motion simulation of an unmanned ocean platform to overcome the limitations of existing unmanned ocean platforms for ocean exploration. The proposed unmanned ocean vehicle combines an unmanned surface vehicle and unmanned underwater [...] Read more.

This paper describes a study on the dynamic modeling and the motion simulation of an unmanned ocean platform to overcome the limitations of existing unmanned ocean platforms for ocean exploration. The proposed unmanned ocean vehicle combines an unmanned surface vehicle and unmanned underwater vehicle with an underwater cable. This platform is connected by underwater cable, and the forces generated in each platform can influence each other’s dynamic motion. Therefore, before developing and operating an unmanned ocean platform, it is necessary to derive a dynamic equation and analyze dynamic behavior using it. In this paper, Newton’s second law and lumped-mass method are used to derive the equations of motion of unmanned surface vehicle, unmanned underwater vehicle, and underwater cable. As the underwater cable among the components of the unmanned ocean platform is expected to affect the motion of unmanned surface vehicle and unmanned underwater vehicle, the similarity of modeling is described by comparing with the cable modeling results and the experimental data. Finally, we constructed a dynamic simulator using Matlab and Simulink, and analyzed the dynamic behavior of the unmanned ocean platform through open-loop simulation. Full article

(This article belongs to the Special Issue Unmanned Marine Vehicles)

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29 pages, 2704 KiB

Open AccessArticle

AutoTuning Environment for Static Obstacle Avoidance Methods Applied to USVs

by Rafael Guardeño, Manuel J. López, Jesús Sánchez and Agustín Consegliere

J. Mar. Sci. Eng. 2020, 8(5), 300; https://doi.org/10.3390/jmse8050300 - 25 Apr 2020

Cited by 10 | Viewed by 3099

Abstract

This work is focused on reactive Static Obstacle Avoidance (SOA) methods used to increase the autonomy of Unmanned Surface Vehicles (USVs). Currently, there are multiple approaches to avoid obstacles, which can be applied to different types of USV. In order to assist in [...] Read more.

This work is focused on reactive Static Obstacle Avoidance (SOA) methods used to increase the autonomy of Unmanned Surface Vehicles (USVs). Currently, there are multiple approaches to avoid obstacles, which can be applied to different types of USV. In order to assist in the choice of the SOA method for a particular vessel and to accelerate the pretuning process necessary for its implementation, this paper proposes a new AutoTuning Environment for Static Obstacle Avoidance (ATESOA) methods applied to USVs. In this environment, a new simplified modelling of a LIDAR (Laser Imaging Detection and Ranging) sensor is proposed based on numerical simulations. This sensor model provides a realistic environment for the tuning of SOA methods that, due to its low load computation, is used by evolutionary algorithms for the autotuning. In order to analyze the proposed ATESOA, three SOA methods were adapted and implemented to consider the measurements given by the LIDAR model. Furthermore, a mathematical model is proposed and evaluated for using as USV in the simulation enviroment. The results obtained in numerical simulations show how the new ATESOA is able to adjust the SOA methods in scenarios with different obstacle distributions. Full article

(This article belongs to the Special Issue Unmanned Marine Vehicles)

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19 pages, 2133 KiB

Open AccessArticle

Robust Output Path-Following Control of Marine Surface Vessels with Finite-Time LOS Guidance

by Lu Wang, Changkui Xu and Jianhua Cheng

J. Mar. Sci. Eng. 2020, 8(4), 275; https://doi.org/10.3390/jmse8040275 - 11 Apr 2020

Cited by 4 | Viewed by 2845

Abstract

This paper proposes a finite-time output feedback methodology for the path-following task of marine surface vessels. First, a horizontal path-following model is established with unknown sideslip angle, unmeasured system state and system uncertainties. A hierarchical control structure is adopted to deal with the [...] Read more.

This paper proposes a finite-time output feedback methodology for the path-following task of marine surface vessels. First, a horizontal path-following model is established with unknown sideslip angle, unmeasured system state and system uncertainties. A hierarchical control structure is adopted to deal with the cascade property. For kinematics system design, a finite-time sideslip angle observer is first proposed, and thus the sideslip angle estimation is compensated in a nonlinear line-of-sight (LOS) guidance strategy to acquire finite-time convergence. For the heading control design, an extended state observer is introduced for the unmeasured state and equivalent disturbance estimation, based on which an output feedback backstepping approach is proposed for the desired tracking of command course angle. The global stability of the cascade system is analyzed. Simulation results validate the effectiveness of the proposed methodology. Full article

(This article belongs to the Special Issue Unmanned Marine Vehicles)

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13 pages, 1533 KiB

Open AccessArticle

Optimized Radial Basis Function Neural Network Based Intelligent Control Algorithm of Unmanned Surface Vehicles

by Renqiang Wang, Donglou Li and Keyin Miao

J. Mar. Sci. Eng. 2020, 8(3), 210; https://doi.org/10.3390/jmse8030210 - 18 Mar 2020

Cited by 29 | Viewed by 3224

Abstract

To improve the tracking stability control of unmanned surface vehicles (USVs), an intelligent control algorithm was proposed on the basis of an optimized radial basis function (RBF) neural network. The design process was as follows. First, the adaptation value and mutation probability were [...] Read more.

To improve the tracking stability control of unmanned surface vehicles (USVs), an intelligent control algorithm was proposed on the basis of an optimized radial basis function (RBF) neural network. The design process was as follows. First, the adaptation value and mutation probability were modified to improve the traditional optimization algorithm. Then, the improved genetic algorithms (GA) were used to optimize the network parameters online to improve their approximation performance. Additionally, the RBF neural network was used to approximate the function uncertainties of the USV motion system to eliminate the chattering caused by the uninterrupted switching of the sliding surface. Finally, an intelligent control law was introduced based on the sliding mode control with the Lyapunov stability theory. The simulation tests showed that the intelligent control algorithm can effectively guarantee the control accuracy of USVs. In addition, a comparative study with the sliding mode control algorithm based on an RBF network and fuzzy neural network showed that, under the same conditions, the stabilization time of the intelligent control system was 33.33% faster, the average overshoot was reduced by 20%, the control input was smoother, and less chattering occurred compared to the previous two attempts. Full article

(This article belongs to the Special Issue Unmanned Marine Vehicles)

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17 pages, 8198 KiB

Open AccessArticle

AUV 3D Path Planning Based on the Improved Hierarchical Deep Q Network

by Yushan Sun, Xiangrui Ran, Guocheng Zhang, Hao Xu and Xiangbin Wang

J. Mar. Sci. Eng. 2020, 8(2), 145; https://doi.org/10.3390/jmse8020145 - 24 Feb 2020

Cited by 51 | Viewed by 4475

Abstract

This study proposed the 3D path planning of an autonomous underwater vehicle (AUV) by using the hierarchical deep Q network (HDQN) combined with the prioritized experience replay. The path planning task was divided into three layers, which realized the dimensionality reduction of state [...] Read more.

This study proposed the 3D path planning of an autonomous underwater vehicle (AUV) by using the hierarchical deep Q network (HDQN) combined with the prioritized experience replay. The path planning task was divided into three layers, which realized the dimensionality reduction of state space and solved the problem of dimension disaster. An artificial potential field was used to design the positive rewards of the algorithm to shorten the training time. According to the different requirements of the task, this study modified the rewards in the training process to obtain different paths. The path planning simulation and field tests were carried out. The results of the tests corroborated that the training time of the proposed method was shorter than that of the traditional method. The path obtained by simulation training was proved to be safe and effective. Full article

(This article belongs to the Special Issue Unmanned Marine Vehicles)

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