Abstract
Mobile robot localization problem is determining a robot's pose in a given environment map. This problem is separated into three categories (position tracking problem, global localization problem, kidnapped problem) based on complexity. This paper presents a new evolutionary localization algorithm for global localization problem. Global localization problem aims to determine the robot's pose in a known environment without initial robot's pose information. In this study, we use differential evolution (DE) as evolutionary computation and define the global localization problem as a global optimization problem. Several studies have reported that differential evolution has an excellent ability for solving global optimization problems. The proposed method has been tested in some simulated environments to demonstrate the effectiveness.
