Designing a Football Team of Robots from Beginning to End
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.
[1] P. Stone, M. Veloso.: Multiagent Systems: A Survey from a Machine
Learning Perspective. Autonomous Robotics volume 8, number 3. July,
2000.
[2] Fiorini, P. and Shiller, Z. (1998), Motion planning in dynamic
environments using velocity obstacles, The International Journal of
Robotics Research 17(7), 760-772.
[3] 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.
[4] 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.
[5] 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.
[6] K. Watanabe. Control of an Omni-directional Mobile Robot. Second
International Conf on Knowledge-based Intelligent Electronic Systems,
Australia, 21-23 April 1998.
[7] 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.
[8] 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.
[9] 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.
[1] P. Stone, M. Veloso.: Multiagent Systems: A Survey from a Machine
Learning Perspective. Autonomous Robotics volume 8, number 3. July,
2000.
[2] Fiorini, P. and Shiller, Z. (1998), Motion planning in dynamic
environments using velocity obstacles, The International Journal of
Robotics Research 17(7), 760-772.
[3] 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.
[4] 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.
[5] 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.
[6] K. Watanabe. Control of an Omni-directional Mobile Robot. Second
International Conf on Knowledge-based Intelligent Electronic Systems,
Australia, 21-23 April 1998.
[7] 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.
[8] 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.
[9] 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.
@article{"International Journal of Electrical, Electronic and Communication Sciences:54727", author = "Maziar A. Sharbafi and Caro Lucas and Aida Mohammadinejad and Mostafa Yaghobi", title = "Designing a Football Team of Robots from Beginning to End", abstract = "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.
", keywords = "multi-agent systems (MAS), Emotional learning, MIMO system, BELBIC, LQR, Communication via environment", volume = "1", number = "7", pages = "991-8", }
