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International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems

IEA/AIE 2021: Advances and Trends in Artificial Intelligence. From Theory to Practice pp 369–378Cite as

Intelligent System of Mooring Planning, Based on Deep Q-Learning

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12799))

Abstract

During processes of globalization, the flow of ships in large marinas is increasing, and parking becomes more difficult for the ship’s captains. Mooring is one of the most complex elements of the control of a ship. This is one of the most dangerous maneuvers requiring analysis of many changing parameters and fast decision-making on the use of various methods of controlling the ship.

Given the modern development of methods for controlling moving objects, these tasks can be solved with the help of intelligent technologies. This article discusses a method for controlling the position of the rudder, speed, and direction of rotation of the ship’s propeller to solve the problem of mooring automation. Of the possible principles used to build control systems, a system based on machine learning algorithms is more suitable. Because it gives the system the ability to make decisions in situations that cannot be described by the algorithm, or its description would be very large. Deep reinforcement learning was chosen as the machine learning algorithm. The article presents the modeling of the proposed method’s functioning. There is given an example of the creation and training of two software agents that form an intelligent scheduler, at their output the required speed and course of a moving sea object is given. The developed control system can be used to automate the mooring of both autonomous and crewed marine moving objects.

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Acknowledgement

The materials of the article were prepared as part of the work on the grant of the Russian Science Foundation No. 16-19-00001 at the Research and Development Institute of Robotics and Control System.

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Authors and Affiliations

  1. Research and Development Institute of Robotics and Control System, Taganrog, Russia

    B. V. Gurenko & M. A. Vasileva

Authors
  1. B. V. Gurenko
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  2. M. A. Vasileva
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Corresponding author

Correspondence to M. A. Vasileva .

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Editors and Affiliations

  1. i-SOMET Incorporate Association, Morioka, Japan

    Hamido Fujita

  2. Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia

    Ali Selamat

  3. Western Norway University of Applied Sciences, Bergen, Norway

    Jerry Chun-Wei Lin

  4. Texas State University San Marcos, San Marcos, TX, USA

    Moonis Ali

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Gurenko, B.V., Vasileva, M.A. (2021). Intelligent System of Mooring Planning, Based on Deep Q-Learning. In: Fujita, H., Selamat, A., Lin, J.CW., Ali, M. (eds) Advances and Trends in Artificial Intelligence. From Theory to Practice. IEA/AIE 2021. Lecture Notes in Computer Science(), vol 12799. Springer, Cham. https://doi.org/10.1007/978-3-030-79463-7_31

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  • DOI: https://doi.org/10.1007/978-3-030-79463-7_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-79462-0

  • Online ISBN: 978-3-030-79463-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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