Prediction-Based Visual Servo Control

  • Michał Walȩcki
  • Cezary Zieliński
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 440)


In service robotics manipulator trajectories must be generated on the run, basing on the information gathered by sensors. This article discusses visual servoing applied to robot arm control, in a task of following a moving object with robot arm. The paper proposes a control system structure based on adaptive Kalman filter prediction algorithm and manipulator joint trajectory generator. Moreover, it shows how to build it using agent-based approach.


Robot object motion tracking Robot visual servo control Robot trajectory generation 



The authors gratefully acknowledge the support of this work by The National Centre for Research and Development grant no. PBS1/A3/8/2012.


  1. 1.
    Staniak, M., Winiarski, T., Zieliński, C.: Parallel visual-force control. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS ’08 (2008)Google Scholar
  2. 2.
    Sanderson, A., Weiss, L.: Adaptive visual servo control of robots. In: Pugh, A. (ed.) Robot Vision, International Trends in Manufacturing Technology. pp. 107–116. Springer, Berlin (1983)Google Scholar
  3. 3.
    Janabi-Sharifi, F., Marey, M.: A kalman-filter-based method for pose estimation in visual servoing. IEEE Trans. Robot. 26(5), 939–947 (2010)CrossRefGoogle Scholar
  4. 4.
    Gortcheva, E., Garrido, R., Gonzalez, E., Carvallo, A.: Predicting a moving object position for visual servoing: theory and experiments. Int. J. Adapt. Control Signal Process. 15(4), 377–392 (2001)CrossRefzbMATHGoogle Scholar
  5. 5.
    Zieliński, C., Winiarski, T.: Motion generation in the MRROC++ robot programming framework. Int. J. Robot. Res. 29(4), 386–413 (2010)CrossRefGoogle Scholar
  6. 6.
    Friedenthal, S., Moore, A., Steiner, R.: A Practical Guide to SysML: The Systems Modeling Language. Morgan Kaufmann (2014)Google Scholar
  7. 7.
    Zieliński, C., Kornuta, T., Winiarski, T.: A systematic method of designing control systems for service and field robots. In: 19th IEEE International Conference on Methods and Models in Automation and Robotics, MMAR’2014, IEEE, pp. 1–14Google Scholar
  8. 8.
    Forsyth, D.A., Ponce, J.: Computer Vision: A Modern Approach. Prentice Hall (2002)Google Scholar
  9. 9.
    Cuevas, E.V., Zaldivar, D., Rojas, R., et al.: Kalman filter for vision tracking (2005)Google Scholar
  10. 10.
    Moose, R., Gholson, N.: Adaptive tracking of abruptly maneuvering targets. In: 1976 IEEE Conference on Decision and Control Including the 15th Symposium on Adaptive Processes, pp. 804–808 (1976)Google Scholar
  11. 11.
    Kiruluta, A., Eizenman, E., Pasupathy, S.: Predictive head movement tracking using a kalman filter. IEEE Trans. Syst. Man Cybern. Part B Cybern. 27(2), 326–331 (1997)CrossRefGoogle Scholar
  12. 12.
    Gutman, P.O., Velger, M.: Tracking targets using adaptive kalman filtering. IEEE Trans. Aerosp. Electron. Syst. 26(5), 691–699 (1990)CrossRefGoogle Scholar
  13. 13.
    Erkorkmaz, K., Altintas, Y.: High speed cnc system design. part i: jerk limited trajectory generation and quintic spline interpolation. Int. J. Mach. Tools Manuf. 41(9), 1323–1345 (2001)CrossRefGoogle Scholar
  14. 14.
    Kroger, T., Tomiczek, A., Wahl, F.M.: Towards on-line trajectory computation. In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 736–741. IEEE (2006)Google Scholar
  15. 15.
    Ramos, F., Gajamohan, M., Huebel, N., D’Andrea, R.: Time-optimal Online Trajectory Generator for Robotic Manipulators. Eidgenössische Technische Hochschule Zürich, Institute for Dynamic Systems and Control (2013)Google Scholar
  16. 16.
    Wilkowski, A., Kornuta, T., Kasprzak, W.: Point-Based Object Recognition in RGB-D Images. In: Filev, D., Jabłkowski, J., Kacprzyk, J., Krawczak, M., Popchev, I., Rutkowski, L., Sgurev, V., Sotirova, E., Szynkarczyk, P., Zadrozny, S. (eds.) Proceedings of the 7th IEEE International Conference Intelligent Systems IS’2014 of Advances in Intelligent Systems and Computing (AISC), vol. 323, pp. 593–604. Springer (2015)Google Scholar
  17. 17.
    Stefańczyk, M., Walçcki, M.: Localization of essential door features for mobile manipulation. In: Szewczyk, R., Zieliński, C., Kaliczyńska, M.,(eds.) Recent Advances in Automation, Robotics and Measuring Techniques of Advances in Intelligent Systems and Computing (AISC), vol. 267, pp. 487–496. Springer (2014)Google Scholar
  18. 18.
    Stefańczyk, M., Kornuta, T.: Handling of asynchronous data flow in robot perception subsystems. In: Simulation, Modeling, and Programming for Autonomous Robots, vol. 8810 of Lecture Notes in Computer Science, pp. 509–520. Springer (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Warsaw University of TechnologyWarsawPoland

Personalised recommendations