@inproceedings{9046ef46850a414b9692dca0a38ff5a8,
title = "Analyses on on-line evolutionary optimization performance for pose tracking while eye-vergence visual servoing",
abstract = "In this research, Genetic algorithm (GA) is used as pose-tracking method 1-step GA, to solve on-line optimization problem for 3-D visual servoing. A correlation function between the target object projected in camera flame and model defined in the control computer is used for a fitness function to be optimized by the 1-step GA. The optimization process for real-time object tracking has been examined on a view point of realizing real-time pose estimation utilizing eye-vergence function. We have confirmed that the 1-step GA optimization method together with eye-vergence correlation fitness function worked cooperatively and how the eye-vergence helped real-time optimization processes in time-domain during visual servoing.",
keywords = "Eye-vergence, GA, Visual servoing",
author = "Kenta Nishimura and Sen Hou and Koichi Maeda and Mamoru Minami and Akira Yanou",
year = "2013",
month = nov,
day = "25",
doi = "10.1109/ICMA.2013.6618001",
language = "English",
isbn = "9781467355582",
series = "2013 IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2013",
pages = "698--703",
booktitle = "2013 IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2013",
note = "2013 10th IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2013 ; Conference date: 04-08-2013 Through 07-08-2013",
}