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Navigation, Modelling and Control of Multiple Marine Autonomous Vehicles
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Navigation, Modelling and Control of Multiple Marine Autonomous Vehicles

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

Deadline for manuscript submissions: closed (20 February 2023) | Viewed by 9578

Special Issue Editor

Prof. Dr. Igor Bychkov

E-Mail Website
Guest Editor
Matrosov Institute for System Dynamics and Control Theory of Siberian Branch of Russian Academy of Sciences, Irkutsk, Russia
Interests: AI; multi-vehicle systems; intelligent control; task allocation and path planning; scheduling; networked decision-making

Special Issue Information

Dear Colleagues,

With the development of unmanned technologies and electronics, marine autonomous vehicles have become the basis for performing a wide range of missions in aquatic environments characterized by numerous perturbations and uncertainties, complex dynamics of objects acting in them, and the presence of hard-to-reach areas. Joint implementation of complex commands by a coordinated group of vehicles can help to accomplish mission goals in an efficient and robust manner, significantly reduce the mission time and its cost, and help to gather more reliable information about the environment. The harsh conditions of marine environments and the need to consider joint motion constraints pose new challenges requiring the development of advanced motion planning and control methods that allow vehicles to cooperate with each other intelligently and adaptively in order to achieve shared mission goals.  

This Special Issue aims to gather recent advances on navigation, guidance, and control of multiple marine autonomous vehicles acting in cooperation. High-quality research papers describing new approaches to cooperative motion planning, localization, and control of multi-vehicle systems, supported by theoretical and experimental studies, are encouraged.

Prof. Dr. Igor Bychkov
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 submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • multi-vehicle systems
  • cooperative path-following and trajectory tracking
  • formation control and stability
  • path planning and obstacle avoidance
  • cooperative navigation and SLAM
  • cooperative situation awareness
  • collaborative dynamic mission planning
  • task allocation, scheduling, and route planning
  • guidance, navigation, and control system design

Published Papers (5 papers)

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Research

18 pages, 1172 KiB  
Article
A Hierarchical Approach to Intelligent Mission Planning for Heterogeneous Fleets of Autonomous Underwater Vehicles
by Maksim Kenzin, Igor Bychkov and Nikolai Maksimkin
J. Mar. Sci. Eng. 2022, 10(11), 1639; https://doi.org/10.3390/jmse10111639 - 3 Nov 2022
Cited by 2 | Viewed by 1745
Abstract
The rapid development of marine robotic technology in recent decades has resulted in significant improvements in the self-sufficiency of autonomous underwater vehicles (AUVs). However, simple scenario-based approaches are no longer sufficient when it comes to ensuring the efficient interaction of multiple autonomous vehicles [...] Read more.
The rapid development of marine robotic technology in recent decades has resulted in significant improvements in the self-sufficiency of autonomous underwater vehicles (AUVs). However, simple scenario-based approaches are no longer sufficient when it comes to ensuring the efficient interaction of multiple autonomous vehicles in complex dynamic missions. The necessity to respond cooperatively to constant changes under severe operating constraints, such as energy or communication limitations, results in the challenge of developing intelligent adaptive approaches for planning and organizing group activities. The current study presents a novel hierarchical approach to the group control system designed for large heterogeneous fleets of AUVs. The high-level core of the approach is rendezvous-based mission planning and is aimed to effectively decompose the mission, ensure regular communication, and schedule AUVs recharging activities. The high-level planning problem is formulated as an original acyclic variation of the inverse shift scheduling problem, which is NP-hard. Since regular schedule adjustments are supposed to be made by the robots themselves right in the course of the mission, a meta-heuristic hybrid evolutionary algorithm is developed to construct feasible sub-optimal solutions in a short time. The high efficiency of the proposed approach is shown through a series of computational experiments. Full article
(This article belongs to the Special Issue Navigation, Modelling and Control of Multiple Marine Autonomous Vehicles)
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21 pages, 8262 KiB  
Article
Data-Driven Prediction of Experimental Hydrodynamic Data of the Manta Ray Robot Using Deep Learning Method
by Jingyi Bai, Qiaogao Huang, Guang Pan and Junjie He
J. Mar. Sci. Eng. 2022, 10(9), 1285; https://doi.org/10.3390/jmse10091285 - 12 Sep 2022
Cited by 1 | Viewed by 2039
Abstract
To precisely control the manta ray robot and improve its swimming and turning speed, the hydrodynamic parameters corresponding to different motion control variables must be tested experimentally. In practice, too many input control parameters will bring thousands of groups of underwater experiments, posing [...] Read more.
To precisely control the manta ray robot and improve its swimming and turning speed, the hydrodynamic parameters corresponding to different motion control variables must be tested experimentally. In practice, too many input control parameters will bring thousands of groups of underwater experiments, posing challenges to the duration and operability of the engineering project. This study proposes a generative adversarial network model to reduce the experimental period by predicting the hydrodynamic experimental time-series data of forces and torques in the three-coordinate directions in a Cartesian coordinate system through different combinations of motion control parameters. The motion control parameters include the rotation amplitude, frequency, and phase difference of the four steering gears which drive the pectoral fins. We designed the prototype and experimental platform and obtained 150 sets of experimental data.To prevent overfitting, the size of the dataset was expanded to 2250 groups by slicing time series, and the subsequences of varying lengths were extended to the same length by LSTM. Finally, the GAN model is used to predict the hydrodynamic time series corresponding to the different motion parameters. The results show that the GAN model can accurately predict the input both from the validation set and the unlearned interpolated motion parameters. This study will save experimental time and cost and provide detailed hydrodynamic experimental data for the precise control of manta rays. Full article
(This article belongs to the Special Issue Navigation, Modelling and Control of Multiple Marine Autonomous Vehicles)
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24 pages, 3463 KiB  
Article
The AUV-Follower Control System Based on the Prediction of the AUV-Leader Movement Using Data from the Onboard Video Camera
by Dmitry Yukhimets and Vladimir Filaretov
J. Mar. Sci. Eng. 2022, 10(8), 1141; https://doi.org/10.3390/jmse10081141 - 18 Aug 2022
Cited by 2 | Viewed by 1400
Abstract
The paper proposes a new method for the synthesis of spatial motion control systems of the AUV-leader and a group of AUV-followers during their cooperative movement in a desired formation. This system allows the provision of an accurate positioning of the followers relative [...] Read more.
The paper proposes a new method for the synthesis of spatial motion control systems of the AUV-leader and a group of AUV-followers during their cooperative movement in a desired formation. This system allows the provision of an accurate positioning of the followers relative to the leader using information received with a low frequency only from their onboard video cameras. To improve the accuracy of the created system it proposes a method of estimation of the parameters of the movement of the AUV-leader (its speeds and accelerations) to predict its movement relative to the AUV-follower in the time intervals between the updates of information received from video cameras. Full article
(This article belongs to the Special Issue Navigation, Modelling and Control of Multiple Marine Autonomous Vehicles)
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20 pages, 8083 KiB  
Article
AUV Formation Coordination Control Based on Transformed Topology under Time-Varying Delay and Communication Interruption
by Juan Li, Huadong Zhang, Tao Chen and Jiaqi Wang
J. Mar. Sci. Eng. 2022, 10(7), 950; https://doi.org/10.3390/jmse10070950 - 11 Jul 2022
Cited by 5 | Viewed by 1343
Abstract
Aiming at the control problem of autonomous underwater vehicle (AUV) pilot-following formation with communication delay and communication interruption, a controller based on feedback linearization and the PD control method is designed in this paper. Firstly, the nonlinear, strongly coupled vehicle model is transformed [...] Read more.
Aiming at the control problem of autonomous underwater vehicle (AUV) pilot-following formation with communication delay and communication interruption, a controller based on feedback linearization and the PD control method is designed in this paper. Firstly, the nonlinear, strongly coupled vehicle model is transformed into a second-order model via the feedback linearization method, and then the formation coordination controller is designed based on consistency theory and the PD control method. The Markov random jump process is used to simulate the formation topology in the event of communication interruption. The condition of stable convergence of the AUV pilot-following formation is analyzed in the presence of time-varying delay and Markov transformation topology. A Lyapunov–Krasovskii equation is established, and linear matrix inequality (LMI) is used to solve the problem of communication interruption and communication delay. The boundary conditions of error convergence of the control system are obtained. Finally, the effectiveness of the formation coordination controller based on the second-order integral model under the unstable conditions of underwater acoustic communication is verified by simulation. Full article
(This article belongs to the Special Issue Navigation, Modelling and Control of Multiple Marine Autonomous Vehicles)
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22 pages, 11533 KiB  
Article
Track Pairs Collision Detection with Applications to Ship Collision Risk Assessment
by Jiahui Shi and Zhengjiang Liu
J. Mar. Sci. Eng. 2022, 10(2), 216; https://doi.org/10.3390/jmse10020216 - 6 Feb 2022
Cited by 10 | Viewed by 1906
Abstract
The port waterway network plays an important role in the organization and management of port ship traffic. Due to limited ship operations, conflicts, congestion, and safety issues often arise in port waters. Conflicts between ships can be predicted by collision detection between ships. [...] Read more.
The port waterway network plays an important role in the organization and management of port ship traffic. Due to limited ship operations, conflicts, congestion, and safety issues often arise in port waters. Conflicts between ships can be predicted by collision detection between ships. A novel collision detection algorithm for trajectory pairs is proposed by introducing variable time interval variables. In addition, to improve the overall accuracy of trajectory compression and reduce redundant calculation in collision detection, a multi-factor Douglas-Peucker algorithm adapted to ship trajectory compression is proposed with the consideration of speed and turn constraints. The maximum speed difference of the algorithm is increased by 1.5–2.5%, and the average speed difference increased by 2.0–4.5%. Based on the method mentioned above, the risk assessment framework of maritime collision is established and the risk situation of the waters near Ningbo Zhoushan Port is evaluated and analyzed by using ship historical track data. Full article
(This article belongs to the Special Issue Navigation, Modelling and Control of Multiple Marine Autonomous Vehicles)
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