Model-Based GA Evolutionary 3-D Position Measurement Method

Abstract:

This paper presents a vision-related technique for 3-D position measurement. The proposed method utilizes the genetic algorithm (GA) and unprocessed grayscale image input from vision, in order to perform recognition of a target being imaged with known target object shape. The problem to recognize the target shape and simultaneous detection of the position, is converted to an optimistic problem of a model-based evaluation function, named as surface-strips model-based fitness function that consists in the computation of the brightness difference between an internal surface and a contour-strips. In order to evaluate the proposed 3-D recognition method, experiments by an unprocessed grayscale image have been input to recognize a ball in the image. The results show the effectiveness of this method for 3-D position detection.

• Periodical: Applied Mechanics and Materials (Volumes 226-228)
• Pages: 1866-1871
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.226-228.1866
• Online since: November 2012
• Authors: Xu Liu, Wei Song, Ya Nan Zhang, Xiao Lei Li
• Keywords: 3D Position Measurement, Genetic Algorithm (GA), Model-Based

References

[1] S. Hutchinson, G. Hager, and P. Corke, Turorial on Visual Servo Control, IEEE Trans. on Robtics and Automation, Vol. 12, No. 5, pp.651-670, (1996).

DOI: 10.1109/70.538972

[2] P. Y. Oh, and P. K. Allen, Visual Servoing by Partitioning Degrees of Freedom, IEEE Trans. on Robtics and Automation, Vol. 17, No. 1, pp.1-17, (2001).

DOI: 10.1109/70.917078

[3] E. Malis, F. Chaumentte, and S. Boudet 2-1/2-D Visual Servoing, IEEE Trans. on Robtics and Automation, Vol. 15, No. 2, pp.238-250, (1999).

DOI: 10.1109/70.760345

[4] P. K. Allen, A. Timchenko, B. Yoshimi, and P. Michelman, Automated Tracking and Grasping of a Moving object with a Robotic Hand-Eye System, IEEE Trans. on Robtics and Automation, Vol. 9, No. 2, pp.152-165, (1993).

DOI: 10.1109/70.238279

[5] K. Sumi, M. Hashimoto, and H. Okuda, Three-level Broad-Edge Matching based Real-time Robot Vision, inProc. IEEE Int. Conf. on Robtics and Automation, pp.1416-1422, (1995).

DOI: 10.1109/robot.1995.525476

[6] N. Okada, and T. Nagata, A Parts Picking System with a Range Finder and a camera System, in Proc. IEEE Int. Conf. on Robtics and Automation, pp.1410-1415, (1995).

DOI: 10.1109/robot.1995.525475

[7] G. Ao, H. Akazawa, M. Izumi, and K. Fukunaga, A Method of Model-Based Object Recognition, in Proc. Japan/USA Symposium on Flexible Automation (ASME), Vol. 2, pp.905-912, (1996).

[8] R. A. Brooks, Model-Based Three-Dimensional Interpretations of Two- Dimensional Images, IEEE Trans. on Pattern Analysis and Machine Intelligence, PAMI-5, 2, pp.140-150, (1983).

DOI: 10.1109/tpami.1983.4767366

[9] M. Minami, H. Suzuki, J. Agbanban, and T. Asakura, Visual Servoing to Fish and Catching Using Global/Local GA Search, 2001 IEEE/ASME Int. Conf. on Advanced Intelligent Mechatronics Proc., pp.183-188, (2001).

DOI: 10.1109/aim.2001.936451

[10] Y. Maeda, and G. Xu, Smoth Matching of Feature and Recovery of Epipolar Equation by Tabu Search, IEICE, Vol. J83-D-2, No. 3, pp.440-448, (1999).

[11] S. Yamane, M. Izumi, and K. Fukunaga, A Method of Model-Based Pose Estimation, IEICE, Vol. J79-D-2, No. 2, pp.165-173, Feb. (1996).

[12] F. Toyama, K. Shoji, and J. Miyamichi, Pose Estimation from a Line Drawing Using Genetic Algorithm, IEICE, Vol. J81-D-2, No. 7, pp.1584-1590, July (1998).