Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
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.
Rocznik
Tom
Strony
745--750
Opis fizyczny
Bibliogr. 11 poz., rys.
Twórcy
autor
- Gdynia Maritime University, Gdynia, Poland
Bibliografia
- 1. Demirel, E. & Bayer, D. 2015. The Further Studies On The COLREGs (Collision Regulations), TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, vol. 9, 17–22.
- 2. Łącki, M. 2007. Machine Learning Algorithms in Decision Making Support in Ship Handling. , Katowice‐Ustroń: WKŁ, p.
- 3. Larkin, D., Kinane, A. & O’Connor, N. 2006. Towards hardware acceleration of neuroevolution for multimedia processing applications on mobile devices , Hong Kong, China.
- 4. 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,.
- 5. Naeem, W., Irwin, G.W. & Aolei, Y. 2012. COLREGs‐based collision avoidance strategies for unmanned surface vehicles , Oxford, ROYAUME‐UNI: Elsevier.
- 6. 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.
- 7. Pietrzykowski, Z. & Małujda, R. 2012. Applicability of fuzzy logic to the COLREG rules interpretation, Zeszyty Naukowe/Akademia Morska W Szczecinie, 109–114.
- 8. Stanley, K.O. & Risto, M. 2002. Efficient Reinforcement Learning Through Evolving Neural Network Topologies.
- 9. 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.
- 10. Szłapczyński, R. & Szłapczyńska, J. 2012. Evolutionary Sets of Safe Ship Trajectories: Evaluation of Individuals, TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, vol. 6, 345–353.
- 11. Wang, T., Yan, X.P., Wang, Y. & Wu, Q. 2017. Ship Domain Model for Multi‐ship Collision Avoidance Decisionmaking with COLREGs Based on Artificial Potential Field, TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, vol. 11, 85–92
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-6f993afc-4b59-45a9-aeff-72f20fa960b4