Genetic Algorithm In Mobile Robot Control

Abstract

GA is one of the intelligent techniques that are used in the robotic filed. It is efficient to design behavioural controllers for mobile robots, such as a GA control simulated mobile robot called Khepera. In this paper we describe three cases of GA which have different chromosome attitudes, structures and objective functions (fitness). These attitudes define the basic robot behaviour and fitness teaches robots to determine his performance's best path with respect to time and distance. The results obtained demonstrate the controlled robot in different obstacle area, and we get performance robots (best path) in developed case genetic algorithm. GA has many problems depending on his condition area but finally he will succeed in obtaining it target.