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Indirect encoding in neuroevolutionary ship handling

Łącki M.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

2018

Vol. 12, no. 1

71--76

In this paper the author compares the efficiency of two encoding schemes for artificial intelligence methods used in the neuroevolutionary ship maneuvering system. 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 an artificial neural network. The helmsman observes input signals derived form an enfironment 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 this project is to evolve a population of helmsmen with indirect encoding and compare results of simulation with direct encoding method.

Multirole Population of Automated Helmsmen in Neuroevolutionary Ship Handling

Lacki M.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

2011

Vol. 5, no. 2

255--260

This paper presents the proposal of advanced intelligent system able to simulate and demon-strate learning behavior of helmsmen in ship maneuvering. Simulated helmsmen are treated as individuals in population, which through environmental sensing learn themselves to safely navigate on restricted waters. In-dividuals are being organized in groups specialized for particular task in ship maneuvering process. Neuroev-olutionary algorithms, which develop artificial neural networks through evolutionary operations, are used in this system.

Wyznaczanie punktów trasy w neuroewolucyjnym sterowaniu statkiem

Łącki M.

Logistyka

2010

nr 6

W artykule tym autor przedstawia system wspomagania decyzji sternika podczas manewrowania statkiem na ograniczonym obszarze. Podstawą systemu są ewolucyjne sztuczne sieci neuronowe, których struktura i parametry, odpowiednio zakodowane w kodzie genetycznym, rozwijają się w czasie symulacji, dostosowując całą populację do wyznaczonego zadania jakim jest bezpieczne przejście statku od pozycji startowej do celu. W celu usprawnienia procesu uczenia sieci neuronowej autor proponuje algorytm wyznaczania punktów trasy będących dodatkowym użytecznym zestawem informacji podczas obliczania wychylenia steru.

In this article the author presents a decision support system for the helmsman maneuvering the vessel in a restricted area. The system is based on evolutionary artificial neural networks, whose structure and parameters, encoded in the genetic code, develop during the simulation by adjusting the entire population to an assignment which is safe passage of the ship from starting point to designated goal. To improve the learning process the author proposes an algorithm of setting waypoints for additional useful data set when calculating the angle of the rudder.