Fuzzy Decision Method for Motion Deadlock Resolving in Robot Soccer Games

  • Hong Liu
  • Fei Lin
  • Hongbin Zha
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4681)


A new method of motion deadlock resolving by using fuzzy decision in robot soccer games is proposed in this paper. For the reasons of complex competition tasks and limited intelligence, soccer robots fall into motion deadlocks in many conditions, which is very difficult for robots to decide whether it is needed to retreat for finding new opportunities. Based on the analysis of human decision for dealing with these kinds of motion deadlocks, the fuzzy decision method is introduced in this paper. Then, fuzzy rules based deadlock resolving system is designed according to relative positions and orientations among robots and the ball in local regions. Lots of experiments by human experts and the fuzzy controller are implemented for comparison. Experimental results show that the method proposed is reasonable and efficient for motion deadlock resolving in most conditions for real soccer robot games.


Fuzzy Decision Soccer Robot Deadlock Resolving 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Jung, M.J., Kim, H.S., Shim, H.S., Kim, J.H.: Fuzzy Rule Extraction for Shooting Action Controller of Soccer Robot. In: IEEE International Systems Conference Proceedings, August 22-25,1999, vol. 1, pp. 556–561 (1999)Google Scholar
  2. 2.
    Sng, H.L, Gupta, G.S., Messom, C.H.: Stragety for Collaboration in Robot Soccer. In: DELTA’02. Proceedings of the First IEEE International Workshop on Electronic Design, Test and Applications, pp. 347–351. IEEE Computer Society Press, Los Alamitos (2002)Google Scholar
  3. 3.
    Wong, C.C., Chou, M.F., Hwang, C.P., Tsai, C.H., Shyu, S.R.: A Method for Obstacle Avoidance and Shooting Action of the Robot Soccer. In: Proceedings of IEEE International Conference on Robotics & Automation, Seoul, Korea, May 21–26, vol. 4, pp. 3778–3782. IEEE Computer Society Press, Los Alamitos (2002)Google Scholar
  4. 4.
    Dadios, E.P., Maravillas, O.A.: Fuzzy Logic for Micro-Robot Soccer Game. In: IECON’01, The 27th Annual Conference of IEEE Industrial Electrical Society, vol. 3, pp. 2154–2159 (2001)Google Scholar
  5. 5.
    Meng, Q.C., Zhuang, X.D.: Game Strategy based on fuzzy logic for Soccer Robots. IEEE Trans. on SMC 5, 3758–3763 (2000)Google Scholar
  6. 6.
    Makita, Y., Hagiwara, M., Nakagawa, M.: A Simple Path Planning System Using Fuzzy Rules and a Potential Field, IEEE World Congress on Computational Intelligence. In: Proceedings of the Third IEEE Conference, vol. 2, pp. 994–999 (1994)Google Scholar
  7. 7.
    Vadakkepat, P., Tan, K.C., Wang, M.L.: Evolutionary Artificial Potential Fields and Their Application in Real Time Robot Path Planning, Evolutionary Computation, 2000. In: Proceedings of the 2000 Congress on vol.1, pp. 16-19, pp. 256- 263 (2000)Google Scholar
  8. 8.
    Wang, L.X., Mendal, J.M.: Generating Fuzzy Rules by Learning from Examples. IEEE Transactions on Systems, Man, and Cybernetics 22(6), 1414–1427 (1992)CrossRefGoogle Scholar
  9. 9.
    Lin, C.T., Lee, G.: Neural-network-based Fuzzy Logic Control and Decision System. IEEE Trans on Computers 40, 1320–1336 (1991)CrossRefGoogle Scholar
  10. 10.
    Parision, T.: Genetic-based New Fuzzy Reasoning Models with Application to Fuzzy Control. IEEE Trans. On SMC 24, 39–47 (1994)Google Scholar
  11. 11.
    Raju, Z.: Hierarchical Fuzzy Control. International Journal of Control 54(5), 1201–1216 (1991)MATHCrossRefGoogle Scholar
  12. 12.
    Gegov, A.E.: Multilayer Fuzzy Control of Multivariable Systems by Direct Decomposition. Int. Journal of systems science 29(8), 851–862 (1998)MATHCrossRefGoogle Scholar
  13. 13.
    Gupta, M.M., Jerzy, B.K., Trojan, G.M.: Multivariable Structure of Fuzzy Control Systems. IEEE trans. on systems, man, and cybernetics SMC-16(5), 638–655 (1986)CrossRefGoogle Scholar
  14. 14.
    Lee, K.K.: An Index of Applicability for the Decompotition Method of Nultivariable Fuzzy Systems. IEEE trans. on fuzzy systems 3, 364–369 (1995)CrossRefGoogle Scholar
  15. 15.
    Walichiewicz, L.: Decomposition of Linguistic Rules in the Design of a Multidimensional Fuzzy Control Algorithm. Cybernetics Syst. Res. 2, 143–148 (1984)Google Scholar
  16. 16.
    Uehara, K.: Parallel and Multistage Fuzzy Inference Based on Families of alpha-level sets, Fuzzy Systems. IEEE Transactions 1, 111–124 (1993)Google Scholar
  17. 17.
    Moon, G.J.: A Method of Converting Conventional Fuzzy Logic System to 2 Layered Hierarchical Fuzzy System. In: The IEEE International Conference on Fuzzy Systems, pp. 1357-1362 (2003)Google Scholar
  18. 18.
    Liu, H., Lin, F., Zha, H.: Competition Analysis System for Soccer Robots based on Global Vision and Trajectory Restrictions. In: Proc. IEEE Int. Conf. on Systems, Man, and Cybernetics (SMC’04), IEEE Computer Society Press, Los Alamitos (2004)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Hong Liu
    • 1
  • Fei Lin
    • 1
  • Hongbin Zha
    • 1
  1. 1.State Key Laboratory of Machine Perception, Peking University, Shenzhen Graduate School 

Personalised recommendations