Abstract
This paper presents a method of improving performance of visual servoing system by integrating inertial sensors to the system. The method is applied to a roll and pitch rotated platform stabilization in high control frequency. For the purpose, an inertial measurement unit is attached to the platform to provide its dynamics information. A new inertial information based feedforward control is used along with the conventional visual feedback control. Two contributions are realized: first, it helps solve the remaining limitation of static-object assumption in conventional visual servoing. Second, it helps drastically improve the response rate of the servoing system due to the utilization of a high-speed inertial measurement unit. Stability of the control system is analyzed such that the error of the system is proved to be bounded. Control algorithm was simulated using Matlab Aerospace Toolbox as well as Robotics Toolbox. Then, experiments were implemented to verify the feasibility of the proposed methodology.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Abbreviations
- 1 f(t), 1 f d ∈ R 2 :
-
Visual feature expressed in image frame and its desired location, respectively
- c v c ≡ [c v c c ω c ]T ∈ R 6 :
-
Cartesian velocity of camera considered in camera coordinate frame {c}
- c b R ∈ R 3×3 :
-
Rotation matrix from frame {c} to frame {b}, follows the convention of Craig7
- b P cORG ∈ R 3 :
-
Position vector of {c} origin with respect to bodyfixed coordinate frame {b}, Craig7
- \(J_V ,\hat J_V \in \Re ^{4k \times 6}\) :
-
Visual Jacobian or interaction matrix between the moving velocity of camera and the velocity of k feature points and its estimated matrix, respectively
- J R (q) ∈ R 6×6 :
-
Robot end-effector Jacobian connect from Cartesian velocity to joint space
References
Chaumette, F. and Hutchinson, S., “Visual Servo Control, Part I: Basic Approaches,” IEEE Robotics & Automation Magazine, Vol. 13, No. 4, pp. 82–90, 2006.
Marchand, E., Spindler, F. and Chaumette, F., “ViSP for visual servoing: a generic software platform with a wide class of robot control skills,” IEEE Robotics & Automation Magazine, Vol. 12, No. 4, pp. 40–52, 2005.
Mariottini, G. L., Oriolo, G. and Prattichizzo, D., “Image-Based Visual Servoing for Nonholonomic Mobile Robots Using Epipolar Geometry,” IEEE Trans. on Robotics, Vol. 23, No. 1, pp. 87–100, 2007.
Corke, P., Lobo, J. and Dias, J., “An Introduction to Inertial and Visual Sensing,” The International Journal of Robotics Research, Vol. 26, No. 6, pp. 519–535, 2007.
Lobo, J. and Dias, J., “Vision and inertial sensor cooperation using gravity as a vertical reference,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 25, No. 12, pp. 1597–1608, 2003.
Chroust, S. G. and Vincze, M., “Fusion of Vision and Inertial Data for Motion and Structure Estimation,” Journal of Field Robotics, Vol. 21, No. 2, pp. 73–83, 2004.
Craig, J. J., “Introduction to robotics mechanics and control, 3rd ed.,” Pearson Educational Inc., 2005.
Kase, H., Maru, N., Nishikawa, A., Yamada, S. and Miyazaki, F., “Visual Servoing of the Manipulator using the Stereo Vision,” International Conference on Industrial Electronics, Control, and Instrumentation, Vol. 3, pp. 1791–1796, 1993.
Chaumette, F. and Hutchinson, S., “Visual Servo Control, Part II: Advanced Approaches,” IEEE Robotics & Automation Magazine, Vol. 14, No. 1, pp. 109–118, 2007.
Remme, E. W., Hoff, L., Halvorsen, P. S., Nærum, E., Skulstad, H., Fleischer, L. A., Elle, O. J. and Fosse, E., “Validation of cardiac accelerometer sensor measurements,” Physiol. Meas., Vol. 30, No. 12, pp. 1429–1444, 2009.
Hol, J. D., Schon, T. B., Gustafsson, F. and Slycke, P. J., “Sensor Fusion for Augmented Reality,” 9th International Conference on Information Fusion, pp. 1–6, 2006.
Papanikolopoulos, N., Khosla, P. and Kanade, T., “Visual tracking of a moving target by a camera mounted on a robot: a combination of vision and control,” IEEE Trans. Robot. Automat., Vol. 9, No. 1, pp. 14–35, 1993.
Espiau, B., Chaumette, F. and Rives, P., “A new approach to visual servoing in robotics,” IEEE Trans. Robot. Automat., Vol. 8, No. 3, pp. 313–326, 1992.
Ginhoux, R., Gangloff, J., Mathelin, M., Soler, L., Sanchez, M. A. and Marescaux, J., “Active filtering of physiological motion in robotized surgery using predictive control,” IEEE Trans. Robot., Vol. 21, No. 1, pp. 67–79, 2005.
Nguyen, H. Q. P., Kang, H.-J. and Suh, Y.-S., “A Visual-Inertial Servoing Method for Tracking Object with Two Landmarks and an Inertial Measurement Unit,” International Journal of Control, Automation and Systems, Vol. 9, No. 2, pp. 317–327, 2011.
Brosilow, C. and Joseph, B., “Techniques of Model-Based Control,” Prentice Hall, 2002.
Nguyen, H. Q. P., Kang, H.-J., Suh, Y.-S. and Ro, Y.-S., “A DCM Based Orientation Estimation Algorithm with an Inertial Measurement Unit and a Magnetic Compass,” Journal of Universal Computer Science, Vol. 15, No. 4, pp. 859–876, 2009.
KAIST Mobile Harbor Center, “Mobile Harbor Project (2009–2012),” http://mobileharbor.or.kr
Hutchinson, S., Hager, G. D. and Corke, P. I., “A tutorial on visual servo control,” IEEE Trans. Robot. Automat., Vol. 12, No. 5, pp. 651–670, 1996.
Corke, P., “A Robotics Toolbox for MATLAB,” IEEE Robotics and Automation Magazine, Vol. 3, No. 1, pp. 24–32, 1996.
Lee, D.-K., Lee, S.-C., Park, S.-H. and Ko, S.-H., “Test and Error Parameter Estimation for MEMS — Based Low Cost IMU Calibration,” Int. J. Precis. Eng. Manuf., Vol. 12, No. 4, pp. 597–603, 2011.
Chatfield, A. B., “Fundamentals of High Accuracy Inertial Navigation,” AIAA, pp. 79–107, 1997.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Nguyen, H.Q.P., Kang, HJ., Suh, YS. et al. A platform stabilization algorithm based on feedforward visual-inertial servoing. Int. J. Precis. Eng. Manuf. 13, 517–526 (2012). https://doi.org/10.1007/s12541-012-0067-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12541-012-0067-6