Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Neural Network-Based Image Moments for Robotic Visual Servoing

  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

This paper applies two Neural Network (NN)-based image features Zhao et al. (2012) to solve the problem of decoupling the rotational velocities around x and y axes of camera frame in robotic visual servoing systems. Based on these two image features and the other four image features used in previous work Chaumette (IEEE Trans. Robot. 20(4):713–723 2004), the interaction matrix has a maximal decoupled structure and thus the singularity of interaction matrix is avoided in Image-Based Visual Servoing (IBVS). The analytical form of depth is given by using classical geometrical primitives and image moment invariants. The IBVS Proportional Derivative (PD) controller is then designed and the stability of the controller is proved by using Lyapunov method. The tracking performance is thus enhanced for a 6 degree-of-freedom (DOF) robotic system. Experimental results on the robotic system are provided to illustrate the effectiveness of the proposed method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Zhao, Y.M., Xie, W.F., Wang, T.T.: Neural network-based image moments for visual servoing of planar objects. In: Proceedings of ASME/IEEE Int. Conference on Advanced Intelligent Mechatronics, pp. 268–274 (2012)

  2. Chaumette, F.: Image moments: A general and useful set of features for visual servoing. IEEE Trans. Robot. 20(4), 713–723 (2004)

    Article  Google Scholar 

  3. Chaumette, F., Hutchinson, S.: Visual servo control, Part I: Basic approaches. IEEE Robot. Autom. Mag. 13(4), 82–90 (2006)

    Article  Google Scholar 

  4. Chaumette, F., Hutchinson, S.: Visual servo control, Part II: Advanced approaches. IEEE Robot. Autom. Mag. 14(1), 109–118 (2007)

    Article  Google Scholar 

  5. Tahri, O., Chaumette, F.: Point-based and region-based image moments for visual servoing of planar objects. IEEE Trans. Robot. 21(6), 1116–1127 (2005)

    Article  Google Scholar 

  6. Hu, M.K.: Visual Pattern Recognition by Moment Invariants. IRE Trans. Info. Theory, IT-8, 179–187 (1962)

  7. Mukundan, P., Ramakrishnan, K.R.: Moment Functions in Image Analysis: Theory and Application. World Scientific, Singapore (1998)

    MATH  Google Scholar 

  8. Prokop, R.J., Reeves, A.P.: A survey of moments-based techniques for unoccluded object representation. Graph. Model. Image Process. 54(5), 438–460 (1992)

    Article  Google Scholar 

  9. Chaumette, F.: Potential problems of stability and convergence in image-based and position-based visual servoing. The Confluence of Vision and Control, vol. 237, pp. 6678. Springer-Verlag, New York (1998)

  10. Malis, E., Chaumette, F., Boudet, S.: 2-1/2 D visual servoing. IEEE Trans. Robot. Autom. 5(2), 238–250 (1999)

    Article  Google Scholar 

  11. Corke, P.I., Hutchinson, S.: A new partitioned approach to image based visual servo control. IEEE Trans. Robot. Autom. 17(4), 507–515 (2001)

    Article  Google Scholar 

  12. Feddema, J.T., Lee, C.S.G., Mitchell, O.R.: Automatic selection of image features for visual servoing of a robot manipulator. In: Proceedings of IEEE Int. Conference on Robotics and Automation, vol. 2, pp. 832–837 (1989)

  13. Nelson, B.J., Khosla, P.K.: The resolvability ellipsoid for visual servoing. In: Proceedings of IEEE Int. Conference on Computer Vision and Pattern Recognition, pp. 829–832 (1994)

  14. Krupa, A., Gangloff, J.: Autonomous retrieval and positioning of surgical instruments in robotized laparoscopic surgery using visual servoing and laser pointers. In: Proceedings of IEEE Int. Conference on Robotics and Automation, pp. 3769–3774 (2002)

  15. Xie, W.F., Li, Z., Tu, X.W., Perron, C.: Switching control of image based visual servoing with laser pointer in robotic manufacturing systems. IEEE Trans. Ind. Electron. 56(2), 520–529 (2009)

    Article  Google Scholar 

  16. Wells, G., Torras, C.: Assessing Image Features for Vision-Based Robot Positioning. J. Intell. Robot. Syst. 30, 95–118 (2001)

    Article  MATH  Google Scholar 

  17. Liu, S.N., Xie, W.F., Su, C.Y.: Image-based visual servoing using improved image moments. In: Proceedings of IEEE Int. Conference on Information and Automation, pp. 577–582 (2009)

  18. Yalcin, B., Ohnishi, K.: Infinite-model neural networks for motion control. IEEE Trans. Ind. Electron. 56(8), 2933–2994 (2009)

    Article  Google Scholar 

  19. Gadoue, S.M., Giaouris, D., Finch, J.W.: Sensorless control of induction motor drives at very low and zero speeds using neural network flux observers. IEEE Trans. Ind. Electron. 8, 3029–3039 (2009)

    Article  Google Scholar 

  20. Xia, C.L., Gao, C., Shi, T.N.: A Neural-network-identifier and Fuzzy-controller-based algorithm foe dynamic decoupling control of Permanent-magnet spherical motor. IEEE Trans. Ind. Electron. 57(8), 2868–2877 (2010)

    Article  Google Scholar 

  21. Cotton, N.J., Wilamowski, B.M.: Compensation of nonlinearities using neural networks implemented on inexpensive microcontrollers. IEEE Trans. Ind. Electron. 58(3), 733–740 (2011)

    Article  Google Scholar 

  22. PUMA 260 Reference Manual, UNIMATE INC (1984)

  23. CM-030 GE/CB-030 GE Users Manual, Document Version 2.0 (2011)

  24. Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice Hall, Englewood Cliffs (1998)

    Google Scholar 

  25. Cheney, E.W., Kincaid, D.R.: Numerical Mathematics and Computing, Cengage Learning (2007)

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical and Industrial Engineering, Concordia University, Montreal, QC, Canada

    Yi-Min Zhao, Wen-Fang Xie & Sining Liu

  2. Department of Mechanical and Electrical Engineering, Hohai University, Changzhou, 214122, China

    Tingting Wang

Authors
  1. Yi-Min Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wen-Fang Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sining Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tingting Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wen-Fang Xie.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, YM., Xie, WF., Liu, S. et al. Neural Network-Based Image Moments for Robotic Visual Servoing. J Intell Robot Syst 78, 239–256 (2015). https://doi.org/10.1007/s10846-014-0065-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-014-0065-2

Keywords

Search

Navigation