A Predictive Evolutionary Algorithm for Dynamic Constrained Inverse Kinematics Problems

  • Patryk Filipiak
  • Krzysztof Michalak
  • Piotr Lipinski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7208)


This paper presents an evolutionary approach to the Inverse Kinematics problem. The Inverse Kinematics problem concerns finding the placement of a manipulator that satisfies certain conditions. In this paper apart from reaching the target point the manipulator is required to avoid a number of obstacles. The problem which we tackle is dynamic: the obstacles and the target point may be moving which necessitates the continuous update of the solution. The evolutionary algorithm used for this task is a modification of the Infeasibility Driven Evolutionary Algorithm (IDEA) augmented with a prediction mechanism based on the ARIMA model.


evolutionary algorithm dynamic function optimization dynamic environment inverse kinematics time series prediction 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Abraham, A., Corchado, E., Corchado, J.M.: Hybrid learning machines. Neurocomputing 72(13-15), 2729–2730 (2009)CrossRefGoogle Scholar
  2. 2.
    Balestrino, A., De Maria, G., Sciavicco, L.: Robust control of robotic manipulators. In: Proceedings of the 9th IFAC World Congress, vol. 5, pp. 2435–2440 (1984)Google Scholar
  3. 3.
    Bertram, D., Kuffner, J., Dillmann, R., Asfour, T.: An Integrated Approach to Inverse Kinematics and Path Planning for Redundant Manipulators. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1874–1879 (2006)Google Scholar
  4. 4.
    Box, G.E.P., Jenkins, G.M.: Time series analysis: Forecasting and control, revised edition. Holden-Day, San Francisco (1976)zbMATHGoogle Scholar
  5. 5.
    Corchado, E., Abraham, A., Carvalho, A.: Hybrid intelligent algorithms and applications. Information Sciences 180(14), 2633–2634 (2010)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Corchado, E., Graña, M., Wozniak, M.: New trends and applications on hybrid artificial intelligence systems. Neurocomputing 75(1), 61–63 (2012)CrossRefGoogle Scholar
  7. 7.
    Fêdor, M.: Application of inverse kinematics for skeleton manipulation in real-time. In: Proceedings of the 19th Spring Conference on Computer Graphics, pp. 203–212. ACM (2003)Google Scholar
  8. 8.
    Garca, S., Fernndez, A., Luengo, J., Herrera, F.: Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Information Sciences 180(10), 2044–2064 (2010)CrossRefGoogle Scholar
  9. 9.
    Goldenberg, A.A., Benhabib, B., Fenton, G.: A complete generalized solution to the inverse kinematics of robots. IEEE Journal of Robotics and Automation RA-1(1) (1985)Google Scholar
  10. 10.
    Goldenberg, A.A., Lawrence, D.L.: A generalized solution to the inverse kinematics of robot manipulators. ASME Journal of Dynamic Systems, Measurement, and Control 107, 103–106 (1985)zbMATHCrossRefGoogle Scholar
  11. 11.
    Hatzakis, I., Wallace, D.: Dynamic multi-objective optimization with evolutionary algorithms: a forward-looking approach. In: Proceedings of the GECCO 2006, pp. 1201–1208. ACM (2006)Google Scholar
  12. 12.
    Karla, P., Mahapatra, P.B., Aggarwal, D.K.: On the solution of multimodal robot inverse kinematics function using real-coded genetic algorithms. In: Proceedings of IEEE International Conference on Systems, Man and Cybernetics, vol. 2, pp. 1840–1845 (2003)Google Scholar
  13. 13.
    Karla, P., Mahapatra, P.B., Aggarwal, D.K.: On the comparison of niching strategies for finding the solution of multimodal robot inverse kinematics. In: Proceedings of IEEE International Conference on Systems, Man and Cybernetics, vol. 6, pp. 5356–5361 (2004)Google Scholar
  14. 14.
    Pedrycz, W., Aliev, R.: Logic-oriented neural networks for fuzzy neurocomputing. Neurocomputing 73(1-3), 10–23 (2009)CrossRefGoogle Scholar
  15. 15.
    Singh, H.K., Isaacs, A., Tapabrata, R.: Infeasibility Driven Evolutionary Algorithm (IDEA) for engineering design optimization. In: Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence, pp. 104–115 (2008)Google Scholar
  16. 16.
    Singh, H.K., Isaacs, A., Nguyen, T.T., Ray, T., Yao, X.: Performance of infeasibility driven evolutionary algorithm (IDEA) on constrained dynamic single objective optimization problems. In: Proceedings of IEEE Congress on Evolutionary Computation, CEC 2009, pp. 3127–3134 (2009)Google Scholar
  17. 17.
    Tabandeh, S., Clark, C., Melek, W.: A genetic algorithm approach to solve for multiple solutions of inverse kinematics using adaptive niching and clustering. In: Proceedings of IEEE Congress on Evolutionary Computation, CEC 2006, pp. 1815–1822 (2006)Google Scholar
  18. 18.
    Wilfong, G.: Motion planning in the presence of movable obstacles. In: Proceedings of ACM Symposium on Computational Geometry, pp. 279–288 (1988)Google Scholar
  19. 19.
    Wolovich, W.A., Elliot, H.: A computational technique for inverse kinematics. In: Proceedings of 23rd IEEE Conference on Decision and Control, pp. 1359–1363 (1984)Google Scholar
  20. 20.
    Zhou, A., Jin, Y., Zhang, Q., Sendhoff, B., Tsang, E.: Prediction-Based Population Re-initialization for Evolutionary Dynamic Multi-objective Optimization. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 832–846. Springer, Heidelberg (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Patryk Filipiak
    • 1
  • Krzysztof Michalak
    • 2
  • Piotr Lipinski
    • 1
  1. 1.Institute of Computer ScienceUniversity of WroclawWroclawPoland
  2. 2.Institute of Business InformaticsWroclaw University of EconomicsWroclawPoland

Personalised recommendations