Mobile Robot Global Localization using Imperialist Competitive Algorithm

Abstract

The most important factor for a mobile robot is navigation. Success in localization is one of the four requirements of navigation which includes perception, localization, cognition and motion control. Localization is usually done in two ways: global localization problem and position tracking problem. In the global localization problem the robot does not know its initial position and it has to localize itself. In the position tracking problem for a robot given its initial position, the goal is to keep track of the position while the robot is navigating through the environment. In this paper we propose a method using an evolutionary algorithm for mobile robots' global localization. We use Imperialist Competitive Algorithm (ICA) to achieve our goal. The method that is used for global localization works in two steps: First we estimate X and Y and then we find the best direction for X and Y which we estimated in previous step. The results which have been demonstrated in the experimental results section show that our algorithm works very accurate in all of the positions that we located the robot for our tests with acceptable number of iterations.