Journal is indexed in following databases:
- SCOPUS
- Web of Science Core Collection - Journal Citation Reports
- EBSCOhost
- Directory of Open Access Journals
- TRID Database - Transportation Research Board
- Index Copernicus Journals Master List
- BazTech
- Google Scholar
2023 Journal Impact Factor - 0.7
2023 CiteScore - 1.4
ISSN 2083-6473
ISSN 2083-6481 (electronic version)
Editor-in-Chief
Associate Editor
Prof. Tomasz Neumann
Published by
TransNav, Faculty of Navigation
Gdynia Maritime University
3, John Paul II Avenue
81-345 Gdynia, POLAND
e-mail transnav@umg.edu.pl
Intelligent Prediction of Ship Maneuvering
1 Gdynia Maritime University, Gdynia, Poland
ABSTRACT: In this paper the author presents an idea of the intelligent ship maneuvering prediction system with the usage of neuroevolution. This may be also be seen as the ship handling system that simulates a learning process of an autonomous control unit, created with artificial neural network. The control unit observes input signals and calculates the values of required parameters of the vessel maneuvering in confined waters. In neuroevolution such units are treated as individuals in population of artificial neural networks, which through environmental sensing and evolutionary algorithms learn to perform given task efficiently. The main task of the system is to learn continuously and predict the values of a navigational parameters of the vessel after certain amount of time, regarding an influence of its environment. The result of a prediction may occur as a warning to navigator to aware him about incoming threat.
KEYWORDS: Intelligent Ship, Ship Manoeuvering, Future of Navigation, Intelligent Prediction, Prediction of Ship Maneuvering, Neuroevolution, Navigational Parameters, Autonomous Control Unit
REFERENCES
Haasdijk, E., Rusu, A.A. & Eiben, A.E. 2010. HyperNEAT for Locomotion Control in Modular Robots.
Isherwood, J.W. 1973. Transactions of Royal Institution on Naval Architects, Wind resistance of merchant ships, vol. 115, 327–332.
Kenneth, S., Nate, K., Rini, S. & Risto, M. 2005. Neuroevolution of an automobile crash warning system , Washington DC, USA.
Larkin, D., Kinane, A. & O’Connor, N. 2006. Towards hardware acceleration of neuroevolution for multimedia processing applications on mobile devices , Hong Kong, China.
Lee, S., Yosinski, J., Glette, K., Lipson, H. & Clune J 2013. Applications of Evolutionary Computing, Evolving gaits for physical robots with the HyperNEAT generative encoding: the benefits of simulation.
Lehman, J. & Miikkulainen, R. 2013. Neuroevolution, Scholarpedia, vol. 8, 30977.
Łącki, M. 2007. Machine Learning Algorithms in Decision Making Support in Ship Handling. , Katowice-Ustroń, WKŁ.
Łącki, M. 2008. Neuroevolutionary approach towards ship handling. , Katowice-Ustroń, WKŁ.
Łącki, M. 2009. Ewolucyjne sieci NEAT w sterowaniu statkiem. Inżynieria Wiedzy i Systemy Ekspertowe, Warszawa: Akademicka Oficyna Wydawnicza EXIT, pp. 535–544.
Nowak, A., Praczyk, T. & Szymak, P. 2008. Zeszyty Naukowe Akademii Marynarki Wojennej, Multi-agent system of autonomous underwater vehicles - preliminary report, vol. 4, 99–108.
OCIMF 1977. Prediction of Wind and Current Loads on VLCCs, Oil Companies International Marine Forum.
Stanley, K.O. & Risto, M. 2002a. Efficient evolution of neural network topologies.
Stanley, K.O. & Risto, M. 2002b. Efficient Reinforcement Learning Through Evolving Neural Network Topologies.
Stanley, K.O., Bryant, B.D. & Risto, M. 2005. IEEE Transaction on Evolutionary Computing, Real-time neuroevolution in the NERO video game, vol. 9, 653–668.
Citation note:
Łącki M.: Intelligent Prediction of Ship Maneuvering. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 10, No. 3, doi:10.12716/1001.10.03.17, pp. 511-516, 2016
Other publications of authors:
File downloaded 1588 times
