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JACIII Vol.15 No.2 pp. 156-163
doi: 10.20965/jaciii.2011.p0156
(2011)

Paper:

Integrated Decision-Making System for Robot Soccer

Ján Vaščák* and Kaoru Hirota**

*Technical University of Košice, Letná 9, 042 00 Košice, Slovakia

**Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan

Received:
December 18, 2010
Accepted:
January 25, 2011
Published:
March 20, 2011
Keywords:
robot soccer, multiagent systems, marketdriven approach, nearness diagram navigation, fuzzy cognitive map
Abstract

This paper deals with the design of an integrated decision-making system for robot soccer. Three main tasks groups of decision-making are discussed based on an analysis of artificial intelligence means used by individual teams – choice of playing strategy, navigation, and kicking. The paper shows that crisp splitting of these decision-making groups is suspicious, and, instead, the use of a convenient implementation means to keep them together is recommended. In this case, fuzzy cognitive maps are introduced whose role is to integrate decision tasks, which is the main contribution of this paper. The paper describes a new design for kicking decisions. The system was successfully tested in the simulator Webots and some concluding remarks are made.

Cite this article as:
J. Vaščák and K. Hirota, “Integrated Decision-Making System for Robot Soccer,” J. Adv. Comput. Intell. Intell. Inform., Vol.15, No.2, pp. 156-163, 2011.
Data files:
References
  1. [1] J. Baltes et al., “The Humanoid Leagues in Robot Soccer Competitions,” Proc. of the IJCAI Workshop on Competitions in Artificial Intelligence and Robotics, Pasadena, California, AAAI Press, pp. 9-16, 2009.
  2. [2] E. Hashemi et al., “MRL Team Description 2010 Standard Platform League, Technical Report,” Mechatronics Research Laboratory, Azad University of Qazvin, Iran, 2010.
    http://www.tzi.de/spl/pub/Website/Teams2010/MRL.pdf
  3. [3] S. Czarnetzki et al., “Nao Devils Dortmund – Team Report for RoboCup 2009,” Technical Report, Robotics Research Institute, Technical University Dortmund, Germany, 2009.
  4. [4] D. García et al., “Borregos RoboCup Standard Platform League 2010 Team Description Paper,” Technical Report, Department of Computer Science, Tecnológico de Monterrey, Mexico, 2010.
    http://www.tzi.de/spl/pub/Website/Teams2010/Borregos.pdf
  5. [5] J. Minguez and L. Montano, “Nearness Diagram Navigation (ND): Collision Avoidance in Troublesome Scenarios,” IEEE Trans. on Robotics and Automation, Vol.20, No.1, pp. 45-59. 2004.
  6. [6] E. Chatzilaris et al., “Kouretes 2010 SPL Team Description Paper,” Technical report, Technical University of Crete, Greece, 2010.
    http://www.tzi.de/spl/pub/Website/Teams2010/Kouretes.pdf
  7. [7] L. E. Parker, “Alliance: An architecture for fault tolerant multirobot cooperation,” IEEE Trans. on Robotics and Automation, Vol.14, No.2, pp. 220-240, 1998.
  8. [8] H. Köse et al., “All Bids for One and One Does for All: Market-Driven Multi-Agent Collaboration in Robot Soccer Domain,” Proc. 18th Int. Symposium of Computer and Information Sciences – ISCIS 2003, Antalya, Turkey, pp. 529-536, 2003.
  9. [9] P. B. Gerkey and M. J. Mataric, “Sold!: Auction methods for multi-robot coordination,” IEEE Trans. on Robotics and Automation, special issue on Advances in Multi-Robot Systems, Vol.18, No.5, pp. 758-786, 2002.
  10. [10] S. Škrjanc et al., “An approach to predictive control of multivariable time-delayed plant: Stability and design issues,” ISA Trans., Vol.43, No.4, pp. 585-595, 2004.
  11. [11] Z. C. Johanyák and S. Kovács, “A brief survey and comparison on various interpolation based fuzzy reasoning methods,” Acta Polytechnica Hungarica, Vol.3, No.1, pp. 91-105, 2006.
  12. [12] J. Minguez, J. Osuna, and L. Montano, “A Divide and Conquer Strategy Based on Situations to Achieve Reactive Collision Avoidance in Troublesome Scenarios,” Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA), New Orleans, USA, pp. 3855-3862, 2004.
  13. [13] C.-C. Yu et al., “Self-Tuning Nearness Diagram Navigation,” Int. Conf. on Service and Interactive Robotics (SIRCon), Taipei, Taiwan, 2009.
  14. [14] B. Kosko, “Fuzzy Cognitive Maps,” Int. J. of Man-Machine Studies, Elsevier, Vol.24, No.1, pp. 65-75, 1986.
  15. [15] S. M. Chen, “Cognitive-Map-based Decision Analysis Based on NPN Logics,” Int. J. Fuzzy Sets and Systems, Elsevier, Vol.71, No.2, pp. 155-163, 1995.
  16. [16] C. Pozna et al., “On the Design of an Obstacle Avoiding Trajectory: Method and Simulation,” Mathematics and Computers in Simulation, Elsevier Science, Vol.79, No.7, pp. 2211-2226, 2009.
  17. [17] J. Vaščák and L. Madarász, “Adaptation of fuzzy cognitive maps – a comparison study,” J. Acta Polytechnica Hungarica, Vol.7, No.3, pp. 109-122, 2010.

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