DOI QR코드

DOI QR Code

A Study on the Development of a Robot Vision Control Scheme Based on the Newton-Raphson Method for the Uncertainty of Circumstance

불확실한 환경에서 N-R방법을 이용한 로봇 비젼 제어기법 개발에 대한 연구

  • 장민우 (조선대학교 기계공학과) ;
  • 장완식 (조선대학교 기계공학과) ;
  • 홍성문 (조선대학교 기계공학과)
  • Received : 2015.10.01
  • Accepted : 2016.01.18
  • Published : 2016.03.01

Abstract

This study aims to develop a robot vision control scheme using the Newton-Raphson (N-R) method for the uncertainty of circumstance caused by the appearance of obstacles during robot movement. The vision system model used for this study involves six camera parameters (C1-C6). First, the estimation scheme for the six camera parameters is developed. Then, based on the six estimated parameters for three of the cameras, a scheme for the robot's joint angles is developed for the placement of a slender bar. For the placement of a slender bar for the uncertainty of circumstances, in particular, the discontinuous robot trajectory caused by obstacles is divided into three obstacle regions: the beginning region, middle region, and near-target region. Then, the effects of obstacles while using the proposed robot vision control scheme are investigated in each obstacle region by performing experiments with the placement of the slender bar.

본 논문은 로봇이 이동하는 동안 장애물이 출현하는 불확실한 환경에서 N-R방법을 이용하여 개발된 로봇 비젼제어 기법의 효율성을 알아보고자 한다. 본 연구에 사용되는 비젼 시스템 모델은 6개의 카메라 매개변수(C1~C6)를 포함한다. N-R방법의 일괄처리기법을 이용하여 사용한 각 카메라에 대한 6개의 카메라 매개변수의 추정을 개발하고, 각 카메라에 대한 6개의 매개변수를 사용한 로봇 관절각 기법을 개발하여 얇은 막대 배치 작업을 수행한다. 특히, 불확실한 환경에서 얇은 막대 배치 작업을 위해 장애물에 의한 불연속 궤적은 시작영역, 중간영역, 타겟 근처 영역 등 3개 영역으로 구분하였다. 제안된 로봇 비젼 제어기법을 사용하여 얇은 막대 배치 실험을 통해 각 장애물 영역에서 장애물의 개수 증가에 따른 영향을 조사하고자 한다.

Keywords

References

  1. Kelly, R., Carelli, R., Nasisis, O., Kuchen, B. and Reyes, F., 2000, "Stable Visual Servoing of Camera-inhand Robotics Systems," IEE/ASME Trans, on Mechatronics, Vol. 5, No. 1, pp. 39-48. https://doi.org/10.1109/3516.828588
  2. Yoshihiro, T., Yasuo, K. and Hiroyuki, I., 1996, "Positioning-Control of Robot Manipulator Using Visual Sensor," Int. Conference on Control, Automation, Robotics and Vision, pp. 894-898.
  3. Berthold, K. P. H., 1986, "Robot vision," Cambridge, Massachusetts, The MIT Press., pp. 46-48.
  4. Peter, A. S., 1993, "Control of Eye and Arm Movements Using Active, Attentional Vision," Applications of AI, Machine Vision and Robotics, pp. 1471-1491.
  5. Skaar, S. B., Brockman, W. H. and Jang, W. S., 1997, "Three-dimensional Camera Space Manipulation," International Journal of Robotics Research, Vol. 9, No. 4, pp. 22-39. https://doi.org/10.1177/027836499000900402
  6. Shahamiri, M. and Jagersand, M., 2005, "Uncalibrated Visual Servoing using a Biased Newton Method for On-line Singularity Detection and Avoidance," IEEE/RSJ International Conference, pp.3953-3958.
  7. Yang, C., Huang, Q., Ogbobe, P. O. and Han, J., 2009, "Forward Kinematics Analysis of Parallel Robots Using global Newton-Raphson Method," Proceedings of 2009 Second ICICTA, pp. 407-410.
  8. Makoto, U. and Koizumi, H., 2012, "A Calculation Method of Photo Voltaic Array's Operational Point for MPPT Evaluation Based on One Variable Newton-Raphson Method," Sustainable Energy Technologies (ICSET), IEEE Third International Conference, pp. 451-456.
  9. Bae, M. J., 2014, "Extraction of Slot Parameters for Waveguide Antennas Using the Newton Raphson Method," Journal of KIIT, Vol. 12, No. 5, pp. 33-41.
  10. Son, J. K., Jang, W. S. and Hong, S. M., 2013, "Evaluation of Two Robot Vision Control Algorithms Developed Based on N-R and EKF Methods for Slender Bar Placement," Trans. Korean Soc. Mech. Eng. A, Vol. 37, No. 4, pp. 447-459. https://doi.org/10.3795/KSME-A.2013.37.4.447
  11. Hong, S. M., 2015, "Development of Robot Vision Control Schemes Using the N-R and EKF Methods for the Moving Target Tracking and Slender Bar Placement Tasks," Thesis of Master of Engineering, School of Mechanical Engineering, Chosun University.
  12. David, F., Robert, P. and Roger, P., 1978, "Statistic," Canada: W.W.Norton, pp.58-59.