Analyses on on-line evolutionary optimization performance for pose tracking while eye-vergence visual servoing

Kenta Nishimura, Sen Hou, Koichi Maeda, Mamoru Minami, Akira Yanou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2013
Pages698-703
Number of pages6
DOIs
Publication statusPublished - 2013
Event2013 10th IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2013 - Takamastu, Japan
Duration: Aug 4 2013Aug 7 2013

Other

Other2013 10th IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2013
CountryJapan
CityTakamastu
Period8/4/138/7/13

Fingerprint

Visual servoing
Genetic algorithms
Computer control
Cameras

Keywords

  • Eye-vergence
  • GA
  • Visual servoing

ASJC Scopus subject areas

  • Artificial Intelligence
  • Electrical and Electronic Engineering
  • Mechanical Engineering

Cite this

Nishimura, K., Hou, S., Maeda, K., Minami, M., & Yanou, A. (2013). Analyses on on-line evolutionary optimization performance for pose tracking while eye-vergence visual servoing. In 2013 IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2013 (pp. 698-703). [6618001] https://doi.org/10.1109/ICMA.2013.6618001

Analyses on on-line evolutionary optimization performance for pose tracking while eye-vergence visual servoing. / Nishimura, Kenta; Hou, Sen; Maeda, Koichi; Minami, Mamoru; Yanou, Akira.

2013 IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2013. 2013. p. 698-703 6618001.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Nishimura, K, Hou, S, Maeda, K, Minami, M & Yanou, A 2013, Analyses on on-line evolutionary optimization performance for pose tracking while eye-vergence visual servoing. in 2013 IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2013., 6618001, pp. 698-703, 2013 10th IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2013, Takamastu, Japan, 8/4/13. https://doi.org/10.1109/ICMA.2013.6618001
Nishimura K, Hou S, Maeda K, Minami M, Yanou A. Analyses on on-line evolutionary optimization performance for pose tracking while eye-vergence visual servoing. In 2013 IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2013. 2013. p. 698-703. 6618001 https://doi.org/10.1109/ICMA.2013.6618001
Nishimura, Kenta ; Hou, Sen ; Maeda, Koichi ; Minami, Mamoru ; Yanou, Akira. / Analyses on on-line evolutionary optimization performance for pose tracking while eye-vergence visual servoing. 2013 IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2013. 2013. pp. 698-703
@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",
doi = "10.1109/ICMA.2013.6618001",
language = "English",
isbn = "9781467355582",
pages = "698--703",
booktitle = "2013 IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2013",

}

TY - GEN

T1 - Analyses on on-line evolutionary optimization performance for pose tracking while eye-vergence visual servoing

AU - Nishimura, Kenta

AU - Hou, Sen

AU - Maeda, Koichi

AU - Minami, Mamoru

AU - Yanou, Akira

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

KW - Eye-vergence

KW - GA

KW - Visual servoing

UR - http://www.scopus.com/inward/record.url?scp=84887983424&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84887983424&partnerID=8YFLogxK

U2 - 10.1109/ICMA.2013.6618001

DO - 10.1109/ICMA.2013.6618001

M3 - Conference contribution

AN - SCOPUS:84887983424

SN - 9781467355582

SP - 698

EP - 703

BT - 2013 IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2013

ER -