Skip to main content
SpringerLink
Log in

Velocity Estimation for Robot Manipulators Using Neural Network

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

Abstract

In robot manipulators, optical incremental encoders are widely used as the transducers to monitor joint position and velocity information. With incremental encoder, positional information is determined as discrete data relative to a reference (home) position. However, velocity information can only be deduced by processing the position data. In this paper, a method of using a neural network to estimate the velocity information of robotic joint from discrete position versus time data is proposed and evaluated. The architecture of the neural net and the training methodology are presented and discussed.

This approach is then applied to estimate the joint velocity of a SCARA robot while performing an electronic component assembly task. Based on computer simulations, comparison of the accuracy of the neural network estimator with two other well established velocity estimation algorithms are made. The neural net approach can maintain good performance even in the presence of measurement noises.

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

Access this article

Log in via an institution

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

References

  1. An, C. H., Atkeson, C. G., Griffiths, J. D., and Hollerbach, J. M.: Experimental evaluation of feedforward and computed torque control, IEEE Trans. Robotics Automat. 5(3) (1989), 368–373.

    Google Scholar 

  2. Brown, R. H., Schneider, S. C., and Mulligan, M. G.: Analysis of algorithms for velocity estimation from discrete position versus time data, IEEE Trans. Industrial Electronics 39(1) (1992), 11–19.

    Google Scholar 

  3. Chan, S. P.:A neural network compensator for uncertainties in robotic assembly, J. of Intelligent and Robotic Systems 13 (1995), 127–141.

    Google Scholar 

  4. Craig, J. J.: Introduction to Robotics: Mechanics and Control, 2nd edn, Addison-Wesley, Reading, MA, 1989.

    Google Scholar 

  5. Guez, A. and Ahmad, Z.: Solution to the inverse kinematics problem in robotics by neural networks, in: Proc. IEEE Conf. on Neural Networks, San Diego, 1988, pp. 617–624.

  6. Harrison, A. J. and McMahan, C. A.: Estimation of acceleration from data with quantization errors using central finite-difference methods, Proc. Inst. Mech. Engrg. 207(1) (1993), 77–86.

    Google Scholar 

  7. Hashimoto, H., Kubota, T., Sato, M., and Harashima, F.: Visual control of robotic manipulator based on neural networks, IEEE Trans. Industrial Electronics 39(6) (1992), 490–496.

    Google Scholar 

  8. Horne, B., Jamshidi, M., and Vadiee, N.: Neural networks in robotics: A survey, J. Intelligent and Robotic Systems 3 (1990), 51–66.

    Google Scholar 

  9. Hunt, K. J., Sbarbaro, D., Zbikowski, R., and Gawthrop, P. J.: Neural networks for control systems — A survey, Automatica 28(6) (1992), 1083–1112.

    Google Scholar 

  10. Khosla, P. K.: Estimation of robot dynamic parameters: Theory and application, Internat. J. Robotics and Automation 3(1) (1988), 35–41.

    Google Scholar 

  11. Kawato, M., Uno, Y., Isobe, M., and Suzuki, R.: Hierarchical neural network model for voluntary movement with application to robotics, IEEE Control Systems Magazine 8 (1988), 8–17.

    Google Scholar 

  12. Luh, J. Y. S., Walker, M. W., and Paul, R. P. C.: On-line computational scheme for mechanical manipulators, J. Dyn. Systems Meas. Control 102 (1980), 69–76.

    Google Scholar 

  13. Markiewicz, B.: Analysis of the computed torque drive method and comparison with conventional position servo for a computer-controlled manipulator, Technical Memo 33–601, Jet Propulsion Laboratory, Pasadena, CA, 1973.

    Google Scholar 

  14. Makino, H. and Furuya, N.: SCARA robot and its family, in: Proc. 3rd Int. Conf. on Assembly Automation, W. Germany, 1982, pp. 433–444.

  15. Miyamoto, H., Kawato, M., Setoyama, T., and Suzuki, R.: Feedback-error-learning neural network for trajectory control of a robotic manipulator, IEEE Trans. Neural Networks 1 (1988), 251–265.

    Google Scholar 

  16. Payeur, P., Le-Huy, H., and Gosselin, C. M.: Trajectory prediction for moving objects using artificial neural networks, IEEE Trans. Industrial Electronics 42(2) (1995), 147–157.

    Google Scholar 

  17. Rumelhart, D., Hinton, G. E., and Williams, R. L.: Learning internal representations by error propagation, in: D. E. Rumelhart and J. L. McClelland (eds), Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Vol. 1: Foundation, MIT Press, Cambridge, MA, 1986.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, Singapore, 639798, Republic of Singapore

    S. P. Chan

Authors
  1. S. P. Chan
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chan, S.P. Velocity Estimation for Robot Manipulators Using Neural Network. Journal of Intelligent and Robotic Systems 23, 147–163 (1998). https://doi.org/10.1023/A:1008022430399

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1008022430399

Search

Navigation