Special Issue "Intelligent Marine Robotics Modelling, Simulation and Applications"

Special Issue Editors

Guest Editor
Prof. Dr. Cheng Siong Chin

Newcastle University in Singapore
Website | E-Mail
Phone: 65 6908 6013
Interests: machine learning design and simulation; interactive virtual simulation using open source platform
Guest Editor
Prof. Dr. Rongxin Cui

School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
Website | E-Mail
Phone: 029-88492945
Interests: underwater vehicles; nonlinear control; path planning

Special Issue Information

Dear Colleagues,

Marine robots are commonly used to carry out several tasks in deeper and riskier areas where divers are not possible. However, there are numbers of challenges in operating them precisely; unpredictable disturbances such as the sea current and wave during actual operation and uncertain dynamic model for designing a typical guidance, navigation, and control system. To circumvent these challenges, the robots have to be intelligent in the sense of having conscious thought to allow them to make decisions that impact their performance and actions. There are many successful applications of artificial intelligent algorithms such as genetic algorithms, neural networks, and fuzzy logic has been proposed for environment exploration, surveillance missions, and collaborative operations. In addition, the implementation of these robots is still facing numerous challenges such as uncertainties in the operating environment, in the field of control systems design, human-robot interaction, fast computational time, robust communication network and rapid implementation with ease of future maintenance in mind.

The aim of this Special Issue should contain recent findings in the field of intelligent robots. Relevant technologies enhancing prototyping, simulation, dexterity, and user experience are also desired. Researchers involved in the marine robotics should find this particular issue extraordinary and provide the latest perspectives on the state-of-the-art.

Topics of interest include (but are not limited to) the following areas:

  • Kinematics, dynamics, and structure design
  • Image-based and signal-based processing
  • Fault identification and detection
  • Signal processing and pattern recognition
  • Communication systems
  • Human–robot interaction
  • Object obstacle avoidance
  • Multi-sensor fusion for control/feedback
  • Intelligent control and automation systems
  • Swarm intelligence and evolutionary algorithms
  • Artificial neural networks
Prof. Dr. Cheng Siong Chin
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 550 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • kinematics
  • dynamics
  • identification
  • communication
  • sensors fusion
  • intelligent control
  • automation systems
  • machine learning methods

Published Papers (6 papers)

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Research

Open AccessArticle
Second Path Planning for Unmanned Surface Vehicle Considering the Constraint of Motion Performance
J. Mar. Sci. Eng. 2019, 7(4), 104; https://doi.org/10.3390/jmse7040104
Received: 11 March 2019 / Revised: 10 April 2019 / Accepted: 11 April 2019 / Published: 17 April 2019
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Abstract
When utilizing the traditional path planning method for unmanned surface vehicles (USVs), ‘planning-failure’ is a common phenomenon caused by the inflection points of large curvatures in the planned path, which exceed the performances of USVs. This paper presents a second path planning method [...] Read more.
When utilizing the traditional path planning method for unmanned surface vehicles (USVs), ‘planning-failure’ is a common phenomenon caused by the inflection points of large curvatures in the planned path, which exceed the performances of USVs. This paper presents a second path planning method (SPP), which is an initial planning path optimization method based on the geometric relationship of the three-point path. First, to describe the motion performance of a USV in conjunction with the limited test data, a method of integral nonlinear least squares identification is proposed to rapidly obtain the motion constraint of the USV merely by employing a zig zag test. It is different from maneuverability identification, which is performed in combination with various tests. Second, the curvature of the planned path is limited according to the motion performance of the USV based on the traditional path planning, and SPP is proposed to make the maximum curvature radius of the optimized path smaller than the rotation curvature radius of the USV. Finally, based on the ‘Dolphin 1’ prototype USV, comparative simulation experiments were carried out. In the experiment, the path directly obtained by the initial path planning and the path optimized by the SPP method were considered as the tracking target path. The artificial potential field method was used as an example for the initial path planning. The experimental results demonstrate that the tracking accuracy of the USV significantly improved after the path optimization using the SPP method. Full article
(This article belongs to the Special Issue Intelligent Marine Robotics Modelling, Simulation and Applications)
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Open AccessArticle
UUV Simulation Modeling and its Control Method: Simulation and Experimental Studies
J. Mar. Sci. Eng. 2019, 7(4), 89; https://doi.org/10.3390/jmse7040089
Received: 27 February 2019 / Revised: 24 March 2019 / Accepted: 25 March 2019 / Published: 28 March 2019
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Abstract
This paper presents the development of an unmanned underwater vehicle (UUV) platform, especially the derivation of the vehicle’s simulation model and its control method to overcome strong sea current. The platform is designed to have a flattened ellipsoidal exterior so as to minimize [...] Read more.
This paper presents the development of an unmanned underwater vehicle (UUV) platform, especially the derivation of the vehicle’s simulation model and its control method to overcome strong sea current. The platform is designed to have a flattened ellipsoidal exterior so as to minimize the hydrodynamic damping on the horizontal plane. Four horizontal thrusters with the identical specifications are symmetrically mounted on the horizontal plane, and each of them has the same thrust dynamics in both forward and reverse directions. In addition, there are three vertical thrusters used to handle the vehicle’s roll, pitch and heave motions. Control strategy proposed in this paper to overcome strong current is that: maximizing the vectored horizontal thrust force against the sea current without or with the least of the vehicle’s rotation on the horizontal plane. For the vehicle model, due to it being symmetric in all of three axes, the vehicle dynamics can be simplified and all of hydrodynamic coefficients are calculated through both of theoretical and empirically-derived formulas. Numerical simulations and experimental studies in both of the water tank and the circulating water channel are carried out to demonstrate the vehicle’s capability of overcoming strong current. Full article
(This article belongs to the Special Issue Intelligent Marine Robotics Modelling, Simulation and Applications)
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Open AccessArticle
Autonomous Minimum Safe Distance Maintenance from Submersed Obstacles in Ocean Currents
J. Mar. Sci. Eng. 2018, 6(3), 98; https://doi.org/10.3390/jmse6030098
Received: 27 June 2018 / Revised: 16 August 2018 / Accepted: 20 August 2018 / Published: 22 August 2018
Cited by 1 | PDF Full-text (10742 KB) | HTML Full-text | XML Full-text
Abstract
A considerable volume of research has recently blossomed in the literature on autonomous underwater vehicles accepting recent developments in mathematical modeling and system identification; pitch control; information filtering and active sensing, including inductive sensors of ELF emissions and also optical sensor arrays for [...] Read more.
A considerable volume of research has recently blossomed in the literature on autonomous underwater vehicles accepting recent developments in mathematical modeling and system identification; pitch control; information filtering and active sensing, including inductive sensors of ELF emissions and also optical sensor arrays for position, velocity, and orientation detection; grid navigation algorithms; and dynamic obstacle avoidance, amongst others. In light of these modern developments, this article develops and compares integrative guidance, navigation, and control methodologies for the Naval Postgraduate School’s Phoenix submerged autonomous vehicle, where these methods are assumed available. The measure of merit reveals how well each of several proposed methodologies cope with known and unknown disturbances, such as currents that can be constant or harmonic, while maintaining a safe passage distance from underwater obstacles, in this case submerged mines. Classical pole-placement designs establish nominal baseline behaviors and are subsequently compared to performance of designs that are optimized to satisfy linear quadratic cost functions in regulators as well as linear-quadratic Gaussian designs. Feed-forward architectures and integral control designs are also evaluated. A noteworthy contribution is a very simple method to mimic optimal results with a “rule of thumb” criteria based on the design’s time constant. Since the rule-of-thumb method uses the assumed system model for computation of the control, it is particularly generic. Cited references each contain methods for online system parameter identification (with a motivation of use in the finding the control signal), permitting the rule of thumb’s generic applicability, since it is expressed in terms of the system parameters. This proposed method permits control design at sea where significant computation abilities are not available. Very simple waypoint guidance is also introduced to guide a vehicle along a preplanned path through a field of obstacles placed at random locations. The linear-quadratic Gaussian design proves best when augmented with integral control, and works well with reduced-order equations, while the “rule of thumb” design is seen to closely mimic the optimal performance. Feed-forward augmentation proves particularly efficient at rejecting constant disturbances, while augmentation with integral control is necessary to counter periodic disturbances, where the augmentations are also optimized in the linear-quadratic Gaussian procedures, yet can be closely mimicked by the proposed “rule of thumb” technique. Full article
(This article belongs to the Special Issue Intelligent Marine Robotics Modelling, Simulation and Applications)
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Open AccessArticle
Coupled and Decoupled Force/Motion Controllers for an Underwater Vehicle-Manipulator System
J. Mar. Sci. Eng. 2018, 6(3), 96; https://doi.org/10.3390/jmse6030096
Received: 10 July 2018 / Revised: 15 August 2018 / Accepted: 17 August 2018 / Published: 21 August 2018
Cited by 1 | PDF Full-text (1813 KB) | HTML Full-text | XML Full-text
Abstract
Autonomous interaction with the underwater environment has increased the interest of scientists in the study of control structures for lightweight underwater vehicle-manipulator systems. This paper presents an essential comparison between two different strategies of designing control laws for a lightweight underwater vehicle-manipulator system. [...] Read more.
Autonomous interaction with the underwater environment has increased the interest of scientists in the study of control structures for lightweight underwater vehicle-manipulator systems. This paper presents an essential comparison between two different strategies of designing control laws for a lightweight underwater vehicle-manipulator system. The first strategy aims to separately control the vehicle and the manipulator and hereafter is referred to as the decoupled approach. The second method, the coupled approach, proposes to control the system at the operational space level, treating the lightweight underwater vehicle-manipulator system as a single system. Both strategies use a parallel position/force control structure with sliding mode controllers and incorporate the mathematical model of the system. It is demonstrated that both methods are able to handle this highly non-linear system and compensate for the coupling effects between the vehicle and the manipulator. The results demonstrate the validity of the two different control strategies when the goal is located at various positions, as well as the reliable behaviour of the system when different environment stiffnesses are considered. Full article
(This article belongs to the Special Issue Intelligent Marine Robotics Modelling, Simulation and Applications)
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Open AccessArticle
A Novel Gesture-Based Language for Underwater Human–Robot Interaction
J. Mar. Sci. Eng. 2018, 6(3), 91; https://doi.org/10.3390/jmse6030091
Received: 30 May 2018 / Revised: 23 July 2018 / Accepted: 27 July 2018 / Published: 1 August 2018
Cited by 1 | PDF Full-text (5210 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The underwater environment is characterized by hazardous conditions that make it difficult to manage and monitor even the simplest human operation. The introduction of a robot companion with the task of supporting and monitoring the divers during their activities and operations underwater can [...] Read more.
The underwater environment is characterized by hazardous conditions that make it difficult to manage and monitor even the simplest human operation. The introduction of a robot companion with the task of supporting and monitoring the divers during their activities and operations underwater can help to solve some of the problems that usually arise in this scenario. In this context, a proper communication between the diver and the robot is imperative for the success of the dive. However, the underwater environment poses a set of technical challenges which are not readily surmountable thus limiting the spectrum from which possibilities can be chosen. This paper presents the design and development of a gesture-based communication language which has been employed for the entire duration of the European project CADDY (Cognitive Autonomous Diving Buddy). This language, the Caddian, was built upon consolidated and standardized underwater gestures that are commonly used in recreational and professional diving. Its use and integration during field tests with a remotely operated underwater vehicle (ROV) is also shown. Full article
(This article belongs to the Special Issue Intelligent Marine Robotics Modelling, Simulation and Applications)
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Open AccessArticle
Fault-Tolerant Control for ROVs Using Control Reallocation and Power Isolation
J. Mar. Sci. Eng. 2018, 6(2), 40; https://doi.org/10.3390/jmse6020040
Received: 7 March 2018 / Revised: 4 April 2018 / Accepted: 10 April 2018 / Published: 12 April 2018
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Abstract
This paper describes a novel thruster fault-tolerant control system (FTC) for open-frame remotely operated vehicles (ROVs). The proposed FTC consists of two subsystems: a model-free thruster fault detection and isolation subsystem (FDI) and a fault accommodation subsystem (FA). The FDI subsystem employs fault [...] Read more.
This paper describes a novel thruster fault-tolerant control system (FTC) for open-frame remotely operated vehicles (ROVs). The proposed FTC consists of two subsystems: a model-free thruster fault detection and isolation subsystem (FDI) and a fault accommodation subsystem (FA). The FDI subsystem employs fault detection units (FDUs), associated with each thruster, to monitor their state. The robust, reliable and adaptive FDUs use a model-free pattern recognition neural network (PRNN) to detect internal and external faulty states of the thrusters in real time. The FA subsystem combines information provided by the FDI subsystem with predefined, user-configurable actions to accommodate partial and total faults and to perform an appropriate control reallocation. Software-level actions include penalisation of faulty thrusters in solution of control allocation problem and reallocation of control energy among the operable thrusters. Hardware-level actions include power isolation of faulty thrusters (total faults only) such that the entire ROV power system is not compromised. The proposed FTC system is implemented as a LabVIEW virtual instrument (VI) and evaluated in virtual (simulated) and real-world environments. The proposed FTC module can be used for open frame ROVs with up to 12 thrusters: eight horizontal thrusters configured in two horizontal layers of four thrusters each, and four vertical thrusters configured in one vertical layer. Results from both environments show that the ROV control system, enhanced with the FDI and FA subsystems, is capable of maintaining full 6 DOF control of the ROV in the presence of up to 6 simultaneous total faults in the thrusters. With the FDI and FA subsystems in place the control energy distribution of the healthy thrusters is optimised so that the ROV can still operate in difficult conditions under fault scenarios. Full article
(This article belongs to the Special Issue Intelligent Marine Robotics Modelling, Simulation and Applications)
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