Mobile robot path planning using genetic algorithms

Genetic Algorithms (GAs) have demonstrated to be effective procedures for solving multicriterion optimization problems. These algorithms mimic models of natural evolution and have the ability to adaptively search large spaces in near-optimal ways. One direct application of this intelligent technique is in the area of evolutionary robotics, where GAs are typically used for designing behavioral controllers for robots and autonomous agents. In this paper we describe a new GA path-planning approach that proposes the evolution of a chromosome attitudes structure to control a simulated mobile robot, called Khepera. These attitudes define the basic robot actions to reach a goal location, performing straight motion and avoiding obstacles. The GA fitness function, employed to teach robot’s movements, was engineered to achieve this type of behavior in spite of any changes in Khepera’s goals and environment. The results obtained demonstrate the controller’s adaptability, displaying near-optimal paths in different configurations of the environment.