Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Incremental Displacement Estimation Algorithm for Real-Time Structural Displacement Monitoring

실시간 구조물 변위 모니터링을 위한 증분형 변위 측정 알고리즘

  • 전해민 (KAIST 건설및환경공학과) ;
  • 신재욱 (KAIST 로봇학제전공) ;
  • 명완철 (KAIST 건설및환경공학과) ;
  • 명현 (KAIST 건설및환경공학과, 로봇학제전공)
  • Received : 2012.04.30
  • Accepted : 2012.05.22
  • Published : 2012.06.01
Download PDF
⟨ Previous Next ⟩

Abstract

The purpose of this paper is to suggest IDE (Incremental Displacement Estimation) algorithm for the previously proposed visually servoed paired structured light system. The system is composed of two sides facing with each other, each with one or two lasers with a 2-DOF manipulator, a camera, and a screen. The 6-DOF displacement between two sides can be estimated by calculating the positions of the projected laser beams and rotation angles of the manipulators. In the previous study, Newton-Raphson or EKF (Extended Kalman Filter) has been used as an estimation algorithm. Although the various experimental tests have validated the performance of the system and estimation algorithms, the computation time is relatively long since aforementioned algorithms are iterative methods. Therefore, in this paper, a non-iterative incremental displacement estimation algorithm which updates the previously estimated displacement with a difference of the previous and the current observed data is introduced. To verify the performance of the algorithm, experimental tests have been performed. The results show that the proposed non-iterative algorithm estimates the displacement with the same level of accuracy compared to the EKF with multiple iterations with significantly less computation time.

Keywords

References

  1. D. Balageas, C. P. Fritzen, and A. Guemes, Structural Health Monitoring, New Jersey: John Wiley & Sons, Inc, 2006.
  2. K. T. Park, S. H. Kim, H. S. Park, and K. W. Lee, "The determination of bridge displacement using measured acceleration," Engineering Structures, vol. 27, no. 3, pp. 371-378, Feb. 2005. https://doi.org/10.1016/j.engstruct.2004.10.013
  3. F. Casciati and C. Fuggini, "Monitoring a steel building using GPS sensors," Smart Structures and Systems, vol. 7, no. 5, pp. 349-363, May 2011. https://doi.org/10.12989/sss.2011.7.5.349
  4. J. J. Lee and M. Shinozuka, "Real-time displacement measurement of a flexible bridge using digital image processing techniques," Experimental Mechanics, vol. 46, no. 1, pp. 105-114, Feb. 2006. https://doi.org/10.1007/s11340-006-6124-2
  5. J. W. Park, J. J. Lee, H. J. Jung, and H. Myung, "Vision-based displacement measurement method for high-rise building structures using partitioning approach," NDT & E International, vol. 43, no. 7, pp. 642-647, Oct. 2010. https://doi.org/10.1016/j.ndteint.2010.06.009
  6. H. Myung., S. M. Lee, and B. J. Lee, "Paired structured light for structural health monitoring robot system," Structural. Health Monitoring, vol. 10, no. 1, pp. 49-64, Jan. 2011 https://doi.org/10.1177/1475921710365413
  7. H. Jeon, Y. Bang, and H. Myung, "A paired visual servoing system for 6-DOF displacement measurement of structures," Smart Materials and Structures, vol. 20, no. 4, p. 45019, Apr. 2011. https://doi.org/10.1088/0964-1726/20/4/045019
  8. H. Jeon, Y. Bang, H. Kim, and H. Myung, "Visual servoing-based paired structured light system for estimation of 6-DOF structural displacement," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 17, no. 10, pp. 989-904, Oct. 2011. https://doi.org/10.5302/J.ICROS.2011.17.10.989
  9. H. Jeon, J. U. Shin, and H. Myung, "Incremental displacement estimation of structures using paired structured light," Smart Structures and Systems, vol. 9, no. 3, pp. 273-286, Mar. 2012. https://doi.org/10.12989/sss.2012.9.3.273
  10. H. Jeon, J. U. Shin, and H. Myung, "Structural displacement monitoring system using an incremental displacement estimation algorithm," Proc. of the 27th ICROS Annual Conference (in Korean), Seoul, Korea, Apr. 2012.
  11. G. Welch and G. Bishop, An introduction to the Kalman filter, TR95-041., Department of Computer Science, University of North Carolina, Chapel Hill, 2006.