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

Abstract

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.