Evolving ant colony system for optimizing path planning in mobile robots

Path planning is one of the problems in robotics. It consists on automatically determine a path from an initial position of the robot to its final position. In this paper we propose a variant of the ant colony system (ACO) applied to optimize the path that a robot can follow to reach its target destination. We also propose to evolve some parameters of the ACO algorithm by using a genetic algorithm (ACO-GA) to optimize the search of the shortest path. We compare the accuracy of ACO against ACO-GA using real environments.