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Using Genetic Algorithms for Optimizing Algorithmic Control System of Biomimetic Underwater Vehicle

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EN
Abstrakty
EN
Autonomous underwater vehicles are vehicles that are entirely or partly independent of human decisions. In order to obtain operational independence, the vehicles have to be equipped with a specialized control system. The main task of the system is to move the vehicle along a path with collision avoidance. Regardless of the logic embedded in the system, i.e. whether it works as a neural network, fuzzy, expert, or algorithmic system or even as a hybrid of all the mentioned solutions, it is always parameterized and values of the system parameters affect its effectiveness. The paper reports the experiments whose goal was to optimize an algorithmic control system of a biomimetic autonomous underwater vehicle. To this end, three different genetic algorithms were used, i.e. a canonical genetic algorithm, a steady state genetic algorithm and a eugenic algorithm.
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autor
  • Institute of Naval Weapon, Polish Naval Academy 81-103 Gdynia, ul. Śmidowicza 69
Bibliografia
  • [1] Alden, M. and Van Kesteren, A. and Miikkulainen, R., Eugenic Evolution Utilizing a Domain Model, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2002), 2002
  • [2] D. E. Goldberg, Genetic algorithms in search, optimization and machine learning, Addison Wesley, Reading, Massachusetts, (1989)
  • [3] Malec M., Morawski M., Zaja˛c J. Fish-like swimming prototype of mobile underwater robot, Journal of Automation, Mobile Robotics & Intelligent Systems, Vol. 4, No 3, 2010, 25-30
  • [4] Polani, D. and Miikkulainen, R., Fast Reinforcement Learning through Eugenic Neuro-Evolution, The University of Texas at Austin, AI 99-277, 1999
  • [5] Polani, D. and Miikkulainen, R., Eugenic Neuro-Evolution for Reinforcement Learning, Proceedings of the Genetic and Evolutionary Computation Conference, 2000
  • [6] T. Praczyk, Using Assembler Encoding to construct Artificial Neural Networks with a modular architecture, Polish Naval Academy, 2011
  • [7] Prior, J. W., Eugenic Evolution for Combinatorial Optimization, The University of Texas at Austin, 1998
  • [8] G. Syswerda, Uniform Crossover in Genetic Algorithms, Proceedings of the 3rd International Conference on genetic Algorithms, 1989
  • [9] Szymak P., Malec M., Morawski M. Directions of development of underwater vehicle with undulating propulsion, Polish Journal of Environmental Studies, Hard Publishing Company, 19(3), 107-110 (2010).
  • [10] P. Szymak, T. Praczyk, Control-oriented Model of Biomimetic Underwater Vehicle Motion, Solid State Phenomena 236 , 121-127 (2015).
  • [11] D. Whitley, A Genetic Algorithm Tutorial, Statistics and Computing 4, 1994, 65-85, http://citeseer.ist.psu.edu
  • [12] http://cmtm.pg.gda.pl/systemy-techniki-glebinowe
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-ecfeb933-a38c-4baf-a072-9cb58079c857
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