A Self-Localization Method Using a Genetic Algorithm Considered Kidnapped Problem
Kaori Watanabe*, Yuehang Ma**, Hitoshi Kono***, and Hidekazu Suzuki***
*New Technology Foundation
Suehiro Bldg. 3F, 3-9-2 Sotokanda, Choyoda-ku, Tokyo 101-0021, Japan
**Graduate School of Engineering, Tokyo Polytechnic University
1583 Iiyama, Atsugi, Kanagawa 243-0297, Japan
***Faculty of Engineering, Tokyo Polytechnic University
1583 Iiyama, Atsugi, Kanagawa 243-0297, Japan
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.
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