A Self-Localization Method Using a Genetic Algorithm Considered Kidnapped Problem

Abstract

The landmark project RoboCup is a well-known international robotics challenge that aims to advance robotics and AI research, with the end goal of developing robots capable of playing a game of soccer autonomously. Self-localization is one of the important elements for an autonomous soccer playing robot because the position information of the robot becomes a determinant of strategic behavior and cooperative operation. Although local searching is accurate, the lack of global searching results in the kidnapped robot problem. Thus, we propose a self-localization method that generates the searching space based on model-based matching using information regarding the white lines on the soccer field. The robot’s position is recognized by optimizing the fitness function using a genetic algorithm (GA). In this report, we adjust the parameter set of the GA on the basis of preliminary experiments and evaluate the accuracy of the proposed self-localization method. We verified that the proposed method enables real-time reversion to correct the position from the kidnapped position using the global/local searching ability of the GA.