Browse > Article
http://dx.doi.org/10.7735/ksmte.2014.23.5.485

A Study on the Robot Vision Control Schemes of N-R and EKF Methods for Tracking the Moving Targets  

Hong, Sung-Mun (Det. of Mechanical Engineering, Chosun University)
Jang, Wan-Shik (Det. of Mechanical Engineering, Chosun University)
Kim, Jae-Meung (Det. of Mechanical Engineering, Chosun University)
Publication Information
Journal of the Korean Society of Manufacturing Technology Engineers / v.23, no.5, 2014 , pp. 485-497 More about this Journal
Abstract
This paper presents the robot vision control schemes based on the Newton-Raphson (N-R) and the Extended Kalman Filter (EKF) methods for the tracking of moving targets. The vision system model used in this study involves the six camera parameters. The difference is that refers to the uncertainty of the camera's orientation and focal length, and refers to the unknown relative position between the camera and the robot. Both N-R and EKF methods are employed towards the estimation of the six camera parameters. Based on the these six parameters estimated using three cameras, the robot's joint angles are computed with respect to the moving targets, using both N-R and EKF methods. The two robot vision control schemes are tested by tracking the moving target experimentally. Given the experimental results, the two robot control schemes are compared in order to evaluate their strengths and weaknesses.
Keywords
Extended Kalman Filtering; Newton-Raphson; Robot vision control scheme; Tracking; Moving target;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Kerr, H. T., 1991, Streamlining Measurement Iteration for EKF Target Tracking, IEEE Transactions on Aerospace and Electronic Systems 27:2 408-421.   DOI   ScienceOn
2 Shademan, A., Janabi-Sharifi, F., 2005, Sensitivity Analysis of EKF and Iterated EKF Pose Estimation for Position-Based Visual Servoing, IEEE Conference on Control Applications Toronto, Canada 755-760.
3 Lippiello, V., Siciliano, B., Villani, L., 2007, Adaptive extended Kalman filtering for visual motion estimation of 3D objects, Control Engineering Practice 15 123-134.   DOI
4 Chen, G., Xia, Z., Ming, X., Lining, S., Ji, J., Du, Z., 2009, Camera Calibration based on Extended Kalman Filter using Robot's Arm Motion, IEEE/ASME International Conference on Advanced Intelligent Mechatronics 1839-1844.
5 Min, K. U., Jang, W. S., 2010, An Experimental Study on the Optimal Arrangement of Cameras used for the Robot's Vision Control Scheme, Journal of the KSMTE 19:1 15-25.
6 Jang, W. S., Kim, K. S., Kim, K. Y., 2004, An Experimental Study on the Optimal number of Cameras used for Vision Control System, Journal of the KSME 13:2 94-103.   과학기술학회마을
7 Junkins, J. L., 1978, An Introduction to Optimal Estimation of Dynamical Systems, Sijthoff and Noordhoff, Alphen Aan Den Rijn 29-33.
8 David, F., Robert, P., Roger, P., 1978, Statistic, W. W. Norton, Canada 58-59.
9 Yoshihiro, T., Yasuo, K., Hiroyuki, I., 1996, Positioning-Control of Robot Manipulator Using Visual Sensor, Int. Conference on Control, Automation, Robotics and Vision 894-898.
10 Bacakoglu, H., Kamel, M., 1997, An Optimized Two-Step Camera Calibration Method, IEEE International Conference on Robotics and Automation 1347-1352.
11 Tsai, R. Y., 1989, Synopsis of recent progress on camera calibration for 3D machine vision, The Robotics Review, Cambridge, MIT Press 146-159.
12 Beardsley, P. A., Zisserman, A., Murray, D. W., 1997, Sequential Updating of Projective and Affine Structure from Motion, International Journal of Computer Vision 23:3 235-259.   DOI   ScienceOn
13 Skaar, S. B., Brockman, W. H., Jang, W. S., 1990, Three-dimensional camera space manipulation, International Journal of Robotics Research 9:4 22-39.   DOI
14 Wedepohl, L. M., Nguyen, H. V., Irwin, G. D., 1996, Frequency- Dependent Transformation Matrices for Untransposed Transmission Line using Newton-Raphson method, IEEE Transactions on Power Systems 11:3 1538-1546.   DOI
15 Kalman, R. E., 1960, A New Approach to Linear Filtering and Prediction Problems, J. basic Rng. Trans. ASEM 82D 35-45.
16 Kalman, R. E., 1963, New Method In Wiener Filtering Theory, John Wiley&Sons Inc., New York 82D 35-45.
17 Piepmeier, J. A., McMurray, G. V., Lipkin, H., 2004, Uncalibrated Dynamic Visual Servoing, IEEE Transactions on Robotics and Automation 20:1 143-147.   DOI
18 Shahamiri, M., Jagersand, M., 2005, Uncalibrated Visual Servoing using a Biased Newton method for On-line Singularity Detection and Avoidance, IEEE/RSJ International Conference 3953-3958.
19 Yang, C., Huang, Q., Ogbobe, P. O., Han, J., 2009, Forward Kinematics Analysis of Parallel Robots Using global Newton-Raphson Method, Proceedings of 2009 Second ICICTA 407-410.
20 Berthold, K. P. H., 1986, Robot vision, Cambridge, Massachusetts, The MIT Press 46-48.
21 Peter, A. S., 1993, Control of eye and arm movements using active, attentional vision, Applications of AI, Machine vision and robotics 1471-1491.
22 Kelly, R., Carelli, O., Nasisis, B., Kuchen., Reyes, F., 2000, Stable Visual servoing of camera-inhand robotics systems, IEEE/ASME Trsns. on Mechatronics 5:1 39-48.   DOI   ScienceOn
23 John, J. C., 1989, Introduction to Robotics mechanics and control, 2nd ed., Addison-Wesley, USA 84.