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2022 Journal Impact Factor - 0.6
2022 CiteScore - 1.7




ISSN 2083-6473
ISSN 2083-6481 (electronic version)




Associate Editor
Prof. Tomasz Neumann

Published by
TransNav, Faculty of Navigation
Gdynia Maritime University
3, John Paul II Avenue
81-345 Gdynia, POLAND
Neuroevolutionary Approach to COLREGs Ship Maneuvers
1 Gdynia Maritime University, Gdynia, Poland
ABSTRACT: The paper describes the usage of neuroevolutionary method in collision avoidance of two power-driven vessels approaching each other regarding COLREGs rules. This may be also be seen as the ship handling system that simulates a learning process of a group of artificial helmsmen - autonomous control units, created with artificial neural networks. The helmsman observes an environment by its input signals and according to assigned CORLEGs rule, he calculates the values of required parameters of maneuvers (propellers rpm and rudder deflection) in a collision avoidance situation. 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 safely and efficiently. The main task of this project is to evolve a population of helmsmen which is able to effectively implement chosen rule: crossing or overtaking.
Łącki, M. 2007. Machine Learning Algorithms in Decision Making Support in Ship Handling. , Katowice-Ustroń: WKŁ, p.
Larkin, D., Kinane, A. & O’Connor, N. 2006. Towards hardware acceleration of neuroevolution for multimedia processing applications on mobile devices , Hong Kong, China. - doi:10.1007/11893295_130
Lee, S., Yosinski, J., Glette, K., Lipson, H. & Clune J 2013. Evolving gaits for physical robots with the HyperNEAT generative encoding: the benefits of simulation, Applications of Evolutionary Computing,. - doi:10.1007/978-3-642-37192-9_54
Naeem, W., Irwin, G.W. & Aolei, Y. 2012. COLREGs-based collision avoidance strategies for unmanned surface vehicles , Oxford, ROYAUME-UNI: Elsevier. - doi:10.1016/j.mechatronics.2011.09.012
Nowak, A., Praczyk, T. & Szymak, P. 2008. Multi-agent system of autonomous underwater vehicles - preliminary report, Zeszty Naukowe Akademii Marynarki Wojennej, vol. 4, 99–108.
Pietrzykowski, Z. & Małujda, R. 2012. Applicability of fuzzy logic to the COLREG rules interpretation, Zeszyty Naukowe/Akademia Morska W Szczecinie, 109–114.
Stanley, K.O. & Risto, M. 2002. Efficient Reinforcement Learning Through Evolving Neural Network Topologies.
Stanley, K.O., Bryant, B.D. & Risto, M. 2005. Real-time neuroevolution in the NERO video game, IEEE Transactions on Evolutionary Computation, vol. 9, 653–668. - doi:10.1109/TEVC.2005.856210
Citation note:
Łącki M.: Neuroevolutionary Approach to COLREGs Ship Maneuvers. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 13, No. 4, doi:10.12716/1001.13.04.06, pp. 745-750, 2019

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