Hand-Eye Calibration and Inverse Kinematics of Robot Arm Using Neural Network

  • Haiyan Wu
  • Walter Tizzano
  • Thomas Timm Andersen
  • Nils Axel Andersen
  • Ole Ravn
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 274)

Abstract

Traditional technologies for solving hand-eye calibration and inverse kinematics are cumbersome and time consuming due to the high nonlinearity in the models. An alternative to the traditional approaches is the artificial neural network inspired by the remarkable abilities of the animals in different tasks. This paper describes the theory and implementation of neural networks for hand-eye calibration and inverse kinematics of a six degrees of freedom robot arm equipped with a stereo vision system. The feedforward neural network and the network training with error propagation algorithm are applied. The proposed approaches are validated in experiments. The results indicate that the hand-eye calibration with simple neural network outperforms the conventional method. Meanwhile, the neural network exhibits a promising performance in solving inverse kinematics.

Keywords

Neural Network Calibration Inverse Kinematics Robot Arm 

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References

  1. 1.
    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  2. 2.
    Wu, H., Zou, K., Zhang, T., Borst, A., Kühnlenz, K.: Insect-inspired high-speed motion vision system for robot control. Biological Cybernetics 106(8-9), 453–463 (2012)CrossRefGoogle Scholar
  3. 3.
    Smisek, J., Jancosek, M., Pajdla, T.: 3d with kinect. In: Consumer Depth Cameras for Computer Vision, pp. 3–25. Springer (2013)Google Scholar
  4. 4.
    Horaud, R., Dornaika, F.: Hand-eye calibration. The International Journal of Robotics Research 14(3), 195–210 (1995)CrossRefGoogle Scholar
  5. 5.
    Daniilidis, K.: Hand-eye calibration using dual quaternions. The International Journal of Robotics Research 18(3), 286–298 (1999)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Strobl, K.H., Hirzinger, G.: Optimal hand-eye calibration. In: The Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4647–4653. IEEE (2006)Google Scholar
  7. 7.
    Spong, M.W., Hutchinson, S., Vidyasagar, M.: Robot modeling and control. John Wiley & Sons, New York (2006)Google Scholar
  8. 8.
    Bayramoglu, E., Andersen, N.A., Ravn, O., Poulsen, N.K.: Pre-trained neural networks used for non-linear state estimation. In: The Proceedings of the 10th International Conference on Machine Learning and Applications and Workshops (ICMLA), vol. 1, pp. 304–310. IEEE (2011)Google Scholar
  9. 9.
    Memon, Q., Khan, S.: Camera calibration and three-dimensional world reconstruction of stereo-vision using neural networks. International Journal of Systems Science 32(9), 1155–1159 (2001)CrossRefMATHGoogle Scholar
  10. 10.
    Ahmed, M.T., Hemayed, E.E., Farag, A.A.: Neurocalibration: a neural network that can tell camera calibration parameters. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, pp. 463–468. IEEE (1999)Google Scholar
  11. 11.
    Tejomurtula, S., Kak, S.: Inverse kinematics in robotics using neural networks. Information Sciences 116(2), 147–164 (1999)CrossRefMATHMathSciNetGoogle Scholar
  12. 12.
    Mayorga, R.V., Sanongboon, P.: Inverse kinematics and geometrically bounded singularities prevention of redundant manipulators: An artificial neural network approach. Robotics and Autonomous Systems 53(3), 164–176 (2005)CrossRefGoogle Scholar
  13. 13.
    Hasan, A.T., Ismail, N., Hamouda, A.M.S., Aris, I., Marhaban, M.H., Al-Assadi, H.: Artificial neural network-based kinematics jacobian solution for serial manipulator passing through singular configurations. Advances in Engineering Software 41(2), 359–367 (2010)CrossRefMATHGoogle Scholar
  14. 14.
  15. 15.
    Bouguet, J.-Y.: Camera calibration toolbox for matlab (2004)Google Scholar
  16. 16.
    Haykin, S.: Neural networks: a comprehensive foundation. Prentice Hall PTR (1994)Google Scholar
  17. 17.
    Norgaard, M.: Neural networks for modelling and control of dynamic systems: A practitioner’s handbook. Springer (2000)Google Scholar
  18. 18.
    Heaton, J.: Introduction to neural networks with Java. Heaton Research St. Louis 200 (2005)Google Scholar
  19. 19.
    Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Cognitive Modeling 1, 213 (2002)Google Scholar
  20. 20.
    Wu, H., Lou, L., Chen, C.-C., Hirche, S., Kolja, K.: Cloud-based networked visual servo control. IEEE Transactions on Industrial Electronics 60(2), 554–566 (2012)CrossRefGoogle Scholar
  21. 21.
    Won, S.-H., Melek, W.W., Golnaraghi, F., et al.: A kalman/particle filter-based position and orientation estimation method using a position sensor/inertial measurement unit hybrid system. IEEE Transactions on Industrial Electronics 57(5), 1787–1798 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Haiyan Wu
    • 1
  • Walter Tizzano
    • 1
  • Thomas Timm Andersen
    • 1
  • Nils Axel Andersen
    • 1
  • Ole Ravn
    • 1
  1. 1.Automation and Control, Department of Electrical EngineeringTechnical University of Denmark, ElektrovejKgs. LyngbyDenmark

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