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Ship course-keeping with neuroevolutionary algorithms

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EN
Abstrakty
EN
The goal of research presented in this article is to check if a neuroevolutionary method with direct encoding is able to be a part of autopilot of the vessel. One of the important tasks of vessel autopilots is to keep a course as straight as possible or to bring the ship back on the route as efficiently as possible. In this paper, the adaptive neuroevolutionary autopilot is described and tested on a simulation model of a ferry. Neuroevolution is a combination of two different but related fields of artificial machine learning: evolution and neural networks. The combined method is very flexible and can be applied to other ship control tasks. The results of computer simulation of the neuroevolutionary course-keeping system have been included.
Rocznik
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70--74
Opis fizyczny
Bibliogr. 14 poz., rys., tab.
Twórcy
autor
  • Gdynia Maritime University
Bibliografia
  • 1. Bagnell, D. & Schneider, J. (2001) Autonomous helicopter control using reinforcement learning policy search methods. International Conference on Robotics and Automation.
  • 2. Collinder, P.A. (1955) A History of Marine Navigation. St. Martin’s Press.
  • 3. Haasdijk, E., Rusu, A.A. & Eiben, A.E. (2010) HyperNEAT for Locomotion Control in Modular Robots. 9th International Conference on Evolvable Systems, 2010.
  • 4. Kenneth, S., Nate, K., Rini, S. & Risto, M. (2005) Neuroevolution of an automobile crash warning system. Proceedings of the 2005 conference on Genetic and evolutionary computation, Washington DC, USA.
  • 5. Łącki, M. (2007) Machine Learning Algorithms in Decision Making Support in Ship Handling. Presented at the TST, Katowice-Ustroń, 2007.
  • 6. Łącki, M. (2009) Ewolucyjne sieci NEAT w sterowaniu statkiem. In InżynieriaWiedzy i SystemyEkspertowe. Warszawa: Akademicka Oficyna Wydawnicza EXIT, pp. 535–544.
  • 7. Larkin, D., Kinane, A. & O’Connor, N. (2006) Towards hardware acceleration of neuroevolution for multimedia processing applications on mobile devices. Proceedings of the 13th international conference on Neural information processing – Volume Part III, Hong Kong, China.
  • 8. 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. In: Esparcia-Alcázar A.I. (eds) Applications of Evolutionary Computation. EvoApplications 2013. Lecture Notes in Computer Science, vol 7835. Springer, Berlin, Heidelberg.
  • 9. maritimbild (2010) VECKANS MARITIMBILD vecka 44 2010 [Online]. Available from: http://maritimbild.com/ AVECKANS_BILD_FILER/VECKANSBILD_44_2010. htm [Accessed: April 24, 2018]
  • 10. Nowak, A., Praczyk, T. & Szymak, P. (2008) Multi-agent system of autonomous underwater vehicles – preliminary report. Zeszyty Naukowe Akademii Marynarki Wojennej 4, pp. 99–108.
  • 11. Stanley, K.O. & Risto, M. (2002) Efficient Reinforcement Learning Through Evolving Neural Network Topologies. Proceedings of the Genetic and Evolutionary Computation Conference.
  • 12. Stanley, K.O., Bryant, B.D. & Risto, M. (2005) Real-time neuroevolution in the NERO video game. IEEE Transactions on Evolutionary Computation 9, 6, pp. 653–668.
  • 13. Tomera, M. (2010) Nonlinear controller design of a ship autopilot. International Journal of Applied Mathematics and Computer Science 20, 2, pp. 271–280.
  • 14. Zwierzewicz, Z. & Borkowski, P. (2006) Tracking System Nonlinear Control Synthesis Under a Lack of Object Dynamical Model. Zeszyty Naukowe Akademii Morskiej w Szczecinie 11 (83), pp. 413–424.
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PL
Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-e81857f4-d975-4180-bd5d-c20eb05ac097
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