Open AccessA Self-Localization Method Using a Genetic Algorithm Considered Kidnapped Problem
Author(s) -
Kaori Watanabe,
Yuehang Ma,
Hitoshi Kono,
Hidekazu Suzuki,
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Publication year - 2022
Publication title -
journal of advanced computational intelligence and intelligent informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.172
H-Index - 20
eISSN - 1343-0130
pISSN - 1883-8014
DOI - 10.20965/jaciii.2022.p0032
Subject(s) - computer science , artificial intelligence , robot , robotics , genetic algorithm , fitness function , position (finance) , field (mathematics) , landmark , set (abstract data type) , matching (statistics) , computer vision , algorithm , machine learning , mathematics , statistics , finance , pure mathematics , economics , programming language
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.