A Realistic Simulator for Humanoid Soccer Robot Using Particle Filter

  • Yao FuEmail author
  • Hamid Moballegh
  • Raúl Rojas
  • Longxu Jin
  • Miao Wang
Part of the Studies in Computational Intelligence book series (SCI, volume 480)


This work presents a realistic simulator called Reality Sim for humanoid soccer robots especially in simulation of computer vision. As virtual training, testing and evaluating environment, simulation platforms have become one significant component in Soccer Robot projects. Nevertheless, the simulated environment in a simulation platform usually has a big gap with the realistic world. In order to solve this issue, we demonstrate a more realistic simulation system which is called Reality Sim with numerous real images. With this system, the computer vision code could be easily tested on the simulation platform. For this purpose, an image database with a large quantity of images recorded in various camera poses is built. Furthermore, if the camera pose of an image is not included in the database, an interpolation algorithm is used to reconstruct a brand-new realistic image of that pose such that a realistic image could be provided on every robot camera pose. Systematic empirical results illustrate the efficiency of the approach while it effectively simulates a more realistic environment for simulation so that it satisfies the requirement of humanoid soccer robot projects.



The authors gratefully acknowledge Daniel Seifert for his knowledge of the project and other members of FUmanoid Team for providing the software base for this work. A video which is relevant to the chapter is linked:


  1. 1.
    H. Kitano et al. RoboCup: a challenge problem for AI and robotics. RoboCup-97: Robot Soccer World Cup I, (Springer, Heidelberg 1998), pp. 1–19Google Scholar
  2. 2.
    K. Asanuma, K. Umeda, R. Ueda, T. Arai, in Development of a Simulator of Environment and Measurement for Autonomous Mobile Robots Considering Camera Characteristics. Proceedings of robot soccer world cup VII (Springer, Heidelberg, 2003)Google Scholar
  3. 3.
    T. Ishimura, T. Kato, K. Oda, T. Ohashi, in An Open Robot Simulator Environment. Proceedings of robot soccer world cup VII (Springer, Heidelberg, 2003)Google Scholar
  4. 4.
    N. Jakobi, Minimal simulations for evolutionary robotics. PhD thesis, University of Sussex, 1998Google Scholar
  5. 5.
    Ziemke, On the role of robot simulations in embodied cognitive science. AISB J. 1(4), 389–399 (2003)Google Scholar
  6. 6.
    M. Young, The Technical Writer’s Handbook. Mill Valley, CA: Juan Cristobal Zagal and Javier Ruiz-del-Solar. Combining simulation and reality in evolutionary robotics. J. Intell. Robot Syst. 50(1), 19–39 (2007)Google Scholar
  7. 7.
    J.C. Zagal, J. Ruiz-del-Solar, in UCHILSIM: A Dynamically and Visually Realistic Simulator for the RoboCup Four Legged League, vol. 3276 . RoboCup 2004: Robot soccer world cup VII, lecture notes in computer science (Springer, Berlin, 2004), pp. 34–45Google Scholar
  8. 8.
    J.C. Zagal, J. Ruiz-del-Solar, P. Vallejos, in Back-to-Reality: Crossing the Reality Gap in Evolutionary Robotics. IAV 2004: Proceedings 5th IFAC symposium on intelligent autonomous Vehicles, Elsevier Science Publishers B.V. AISB J. 1(4), 389–399 (2004)Google Scholar
  9. 9.
    J.C. Bongard, H. Lipson, in Once More Unto the Breach: Co–Evolving a Robot and its Simulator. Proceedings of the ninth international conference on the simulation and synthesis of living systems (ALIFE9), pp. 57–62Google Scholar
  10. 10.
    L. Iocchi, F. Dalla Libera, E. Menegatti, in Learning Humanoid Soccer Actions Interleaving Simulated and Real Data. Proceedings of the second workshop on humanoid soccer robots IEEE-RAS 7th international conference on humanoid robots, Pittsburgh, 2007Google Scholar
  11. 11.
    B Fischer et al. FUmanoid team description paper 2010. (Workshop Robocup Singapore 2010)Google Scholar
  12. 12.
    S. Thrun, D. Fox, W. Burgard, F. Dellaert. Robust Monte Carlo localization for mobile robots. Artif. Intell. 128(1–2), 99-141 (2001)Google Scholar
  13. 13.
    R.E. Kalman, A new approach to linear filtering and predictionproblems. J. Basic Eng. 82(1), 35–45 (1960)CrossRefGoogle Scholar
  14. 14.
    M.J. Quinlan, R.H. Middleton, in Comparison of Estimation Techniques Using Kalman Filter and Grid-Based Filter for Linear and Non-Linear System. Proceedings of the international conference on computing: Theory and applications technique for RoboCup soccer (ICCTA2007) (1960)Google Scholar
  15. 15.
    M.J. Quinlan, R.H. Middleton. Multiple model kalman filters: a localization technique for RoboCup soccer. Lect. Notes Comput. Sci. 5949, 276–287 (2010)Google Scholar
  16. 16.
    S. Thrun, W. Burgard, D. Fox, Probabilistic Robotics (MIT Press, Cambridge, 2005)zbMATHGoogle Scholar
  17. 17.
    A.De Doucet, N. Freitas, N.J. Gordon, Sequential Monte Carlo Methods in Practice (Springer, Heidelberg, 2001)zbMATHCrossRefGoogle Scholar
  18. 18.
    M.S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, A tutorial on particle filters for on line nonlinear/non-gaussian bayesian tracking. IEEE Trans. Sig. Process. 50(2) (2002)Google Scholar
  19. 19.
    T. Laue, T. Röfer, in Pose Extraction from Sample Sets in Robot Self-localization-a Comparison and a Novel Approach. Proceedings of the 4th European conference on mobile robots—ECMR’09, (Mlini/Dubrovnik, Croatia, 2009), pp. 283–288Google Scholar
  20. 20.
    T. Langner, Selbstlokalisierung für humanoide Fußballroboter mittels Mono-und Stereovision. Master thesis. FU Berlin, FB Mathematik und Informatik, Berlin. September 2009 (in German)Google Scholar
  21. 21.
    R. Douc, O. Cappe, E. Moulines, in Comparison of Resampling Schemes for Particle Filtering. ISPA 2005. Proceedings of the 4th international symposium on image and signal processing and analysis (2005), pp. 64–69Google Scholar
  22. 22.
    A. Desrosières, The Politics of Large Numbers: a History of Statistical Reasoning, Trans. Camille Naish (Harvard University Press, United State, 2004)Google Scholar
  23. 23.
    A. Björck, Numerical Methods for Least Squares Problems (SIAM, Philadelphia, 1996)zbMATHCrossRefGoogle Scholar
  24. 24.
    J. Nocedal, J. Stephen, Wright Numerical Optimization (Springer, Heidelberg, 1999)CrossRefGoogle Scholar
  25. 25.
    D. Serfert et al. FUmanoid team description paper 2011. Workshop RoboCup Istanbul (2011)Google Scholar
  26. 26.
    RoboCup soccer humanid league rules and setup,

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yao Fu
    • 1
    • 2
    • 3
    Email author
  • Hamid Moballegh
    • 3
  • Raúl Rojas
    • 3
  • Longxu Jin
    • 1
  • Miao Wang
    • 3
  1. 1.ChangChun Institute of Optics, Fine Mechanics and PhysicsChinese Academe of SciencesChangChunChina
  2. 2.Graduate University of Chinese Academe of SciencesBeijingChina
  3. 3.Department of Mathematics and Computer ScienceFreie Universität BerlinBerlinGermany

Personalised recommendations