Published July 25, 2007 | Version 5638
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Designing a Football Team of Robots from Beginning to End

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The Combination of path planning and path following is the main purpose of this paper. This paper describes the developed practical approach to motion control of the MRL small size robots. An intelligent controller is applied to control omni-directional robots motion in simulation and real environment respectively. The Brain Emotional Learning Based Intelligent Controller (BELBIC), based on LQR control is adopted for the omni-directional robots. The contribution of BELBIC in improving the control system performance is shown as application of the emotional learning in a real world problem. Optimizing of the control effort can be achieved in this method too. Next the implicit communication method is used to determine the high level strategies and coordination of the robots. Some simple rules besides using the environment as a memory to improve the coordination between agents make the robots' decision making system. With this simple algorithm our team manifests a desirable cooperation.

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References

  • P. Stone, M. Veloso.: Multiagent Systems: A Survey from a Machine Learning Perspective. Autonomous Robotics volume 8, number 3. July, 2000.
  • Fiorini, P. and Shiller, Z. (1998), Motion planning in dynamic environments using velocity obstacles, The International Journal of Robotics Research 17(7), 760-772.
  • J.-P. Laumond, Ed., Robot Motion Planning and Control, chapter Feedback control of a nonholonomic car-like robot, by A. DeLuca, G. Oriolo, C. Samson, Springer-Verlag, ISBN 3-540-76219-1, available from http://www.laas.fr/»jpl/book.html, 1998.
  • Manu Chhabra, Anusheel Nahar, Nishant Agrawal, Tamhant Jain, Amitabha Mukerjee, Apurva Mathad and Siddhartha Chaudhuri, Novel Approaches to Vision and Motion Control for Robot Soccer, National Conference on Advanced Manufacturing and Robotics, CMERI, Durgapur, 2004.
  • M. J. Jung, H. S. Kim, S. Kim, J. H. Kim. Omni-directional mobile based OK-I1. in Proc International Conference on Robotics & Automation, San Francisco, April 2000, 3449-3454.
  • K. Watanabe. Control of an Omni-directional Mobile Robot. Second International Conf on Knowledge-based Intelligent Electronic Systems, Australia, 21-23 April 1998.
  • C. H. Xu, H. M. Li, X. H. Xu. A Composite Controllers Based on fuzzy Rules and Neural Network for Soccer Robot System. in Proc of the first International Conf on Machine Learning and Cybernetics, Beijing, 4-5 Nov 2002.
  • T. H. Lee, H. K. Lam, F. H. F. Leung, P. K. S. Tam. A Practical Fuzzy Logic Controller for the Path Tracking of Wheeled Mobile Robots. IEEE Control System Magazine, April 2003, pp. 60-65.
  • F. Cuesta, A. Ollero, Fuzzy control of reactive navigation withstability analysis based on Conicity and Lyapunov theory, Control Engineering Practice 12 (2004) 625-638. [10] O. Nelles. Orthonormal Basis Functions for Nonlinear System Identification with Local Linear Model Trees (LoLiMoT). in Proc. IFAC Symposium on System Identification, Kitakyushu, Fukuoka, Japan, 1997. [11] J. Moren, C. Balkenius. A Computational Model of Emotional conditioning in the Brain. in Proc. workshop on Grounding Emotions in Adaptive Systems, Zurich, 1998. [12] J. Moren, C. Balkenius. A Computational Model of Emotional Learning in The Amygdala: From animals to animals. in Proc. 6th International conference on the simulation of adaptive behavior, Cambridge, Mass., The MIT Press, 2000. [13] R. M. Milasi, C. Lucas, and B. N. Araabi. Speed Control of an Interior Permanent Magnet Synchronous Motor Using BELBIC (Brain Emotional Learning Based Intelligent Controller). In M. Jamshidi, L. Foulloy, A. Elkamel, and J. S. Jamshidi (eds.), Intelligent Automations and Control- Trends, Principles, and Applications. Albuquerque, NM, USA: TSI Press Series: Proceedings of WAC, 16, M. Jamshidi (series editor), 2004. 280- 286. [14] R. Mohammadi Milasi, C. Lucas, and B. N. Araabi. A Novel Controller for a Power System Based BELBIC (Brain Emotional Learning Based Intelligent Controller). In M. Jamshidi, L. Foulloy, A. Elkamel, and J. S. Jamshidi (eds.), Intelligent Automations and Control- Trends, Principles, and Applications. Albuquerque, NM, USA: TSI Press Series: Proceedings of WAC, 16, M. Jamshidi (series editor), 2004. 409- 420. [15] C. Lucas, F. Rashidi, and J. Abdi, "Transient Stability Improvement in Power Systems via Firing Angle Control of TSCS Using Context Based Emotional Controller", Intelligent Automations and Control Trends, Principles, and Applications, Albuquerque, NM, USA: TSI Press Series: Proceedings of WAC, No.16, 2004. [16] C. Lucas, D. Shahmirzadi, N. Sheikholeslami. Introducing BELBIC: Brain Emotional Learning Based Intelligent Controller. International Journal of Intelligent Automation and Soft Computing, Vol. 10, No. 1, 2004, pp. 11-22. [17] Choomuang, R. & Afzulpurkar, N. / Hybrid Kalman Filter/Fuzzy Logic based Position Control of Autonomous Mobile Robot, pp. 197 - 208, International Journal of Advanced Robotic Systems, Volume 2, Number 3 (2005), ISSN 1729-8806. [18] C. Lucas, F. Rashidi, and J. Abdi, Transient Stability Improvement in Power Systems via Firing Angle Control of TSCS Using Context Based Emotional Controller. Intelligent Automations and Control- Trends, Principles, and Applications. Albuquerque, NM, USA: TSI Press Series: Proceedings of WAC, 16, M. Jamshidi (series editor), 2004. 37- 42. [19] J. Abdi, Gh. F. Khalili, M. Fatourechi, C. Lucas, and A. Khaki Sedigh. Control of Multivariable Systems Based on Emotional Temporal Difference Learning Controller. International Journal of Engineering, Transactions A: Basics, 17 (4), November 2004. 357- 370. [20] A. Gholipour, C. Lucas, and D. Shahmirzadi. Purposeful Prediction of Space Weather Phenomena by Simulated Emotional Learning. International Journal of Modeling and Simulation, 24 (2), 2004, 65- 72. [21] M. Fatourechi, C. Lucas, A. Khaki Sedigh. Emotional Learning as a New Tool for Development of Agent based System. Informatica, 27(2), June 2003, 137-144. [22] C. Lucas, A. Abbaspour, A. Gholipour, B. Nadjar Araabi, M. Fatourechi. Enhancing the Performance of Neurofuzzy Predictors by Emotional Learning Algorithm. Informatica, 27(2), June 2003, 165-174. [23] P. P. Grass'e.: La reconstruction du nid et les coordinations interindividuelles chez Bellicositermes natalensis et Cubitermes sp. La th'eorie de la stigmergie: Essai d-interpretation des termites constructeurs. Insectes Sociaux, 6, 41-83, 1959. [24] R. Beckers, O. Holland, J. L. Deneubourg.: From local actions to global tasks: Stigmergy and collective robotics. H. Ritter, H. Cruse, J. Dean, (ed) Prerational Intelligence: Adaptive behavior and intelligent systems without symbols and logic, (Kluwer Academic Publishers). [25] R. Daneshvar, C. Lucas.: Improving Reinforcement Learning Algorithm Using Emotions in a Multi-agent System. In Th. Rist, R. Aylett, D. Ballin, J. Rickel (eds.) Intelligent Virtual Agents, Berlin: Springer, Lecture Notes in Artificial Intelligence, 2792, J. G. Carbonell and J. Siekmann (subseries eds.), 2003. [26] W. Sheng, Q. Yang, S. Ci, N. Xi.: Multi-robot Area Exploration with Limited-range Communications. Proceedings of 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, September 28-October 2, 2004, Sendai, Japan. [27] O. Holland, C. Melhuish.: Stigmergy, self-organisation, and sorting in collective robotics. Artificial Life, 5:2 (1999) pp. 173-202. [28] Igor Verner. RoboCup: A challenging environment for engineering education. In Minoru Asada and Hiroaki Kitano, editors, RoboCup-98: Robot Soccer World Cup II. Springer Verlag, Berlin, 1999. [29] A. Bryan Loyall, Believable Agents: Building Interactive Personalities, PhD Thises, CMU-CS-97-123, May 1997.