Interval Type-2 Fuzzy Logic Control of Mobile Robots

Affiliation(s)

REGIM: Research Group on Intelligent Machines, National Engineering School of Sfax (ENIS), Sfax, Tunisia;.

Centre for Computational Intelligence, De Montfort University, Leicester, UK..

REGIM: Research Group on Intelligent Machines, National Engineering School of Sfax (ENIS), Sfax, Tunisia;.

Centre for Computational Intelligence, De Montfort University, Leicester, UK..

ABSTRACT

Navigation of autonomous mobile robots in dynamic and unknown environments needs to take into account different kinds of uncertainties. Type-1 fuzzy logic research has been largely used in the control of mobile robots. However, type-1 fuzzy control presents limitations in handling those uncertainties as it uses precise fuzzy sets. Indeed type-1 fuzzy sets cannot deal with linguistic and numerical uncertainties associated with either the mechanical aspect of robots, or with dynamic changing environment or with knowledge used in the phase of conception of a fuzzy system. Recently many researchers have applied type-2 fuzzy logic to improve performance. As control using type-2 fuzzy sets represents a new generation of fuzzy controllers in mobile robotic issue, it is interesting to present the performances that can offer type-2 fuzzy sets by regards to type-1 fuzzy sets. The paper presented deep and new comparisons between the two sides of fuzzy logic and demonstrated the great interest in controlling mobile robot using type-2 fuzzy logic. We deal with the design of new controllers for mobile robots using type-2 fuzzy logic in the navigation process in unknown and dynamic environments. The dynamicity of the environment is depicted by the presence of other dynamic robots. The performances of the proposed controllers are represented by both simulations and experimental results, and discussed over graphical paths and numerical analysis.

Navigation of autonomous mobile robots in dynamic and unknown environments needs to take into account different kinds of uncertainties. Type-1 fuzzy logic research has been largely used in the control of mobile robots. However, type-1 fuzzy control presents limitations in handling those uncertainties as it uses precise fuzzy sets. Indeed type-1 fuzzy sets cannot deal with linguistic and numerical uncertainties associated with either the mechanical aspect of robots, or with dynamic changing environment or with knowledge used in the phase of conception of a fuzzy system. Recently many researchers have applied type-2 fuzzy logic to improve performance. As control using type-2 fuzzy sets represents a new generation of fuzzy controllers in mobile robotic issue, it is interesting to present the performances that can offer type-2 fuzzy sets by regards to type-1 fuzzy sets. The paper presented deep and new comparisons between the two sides of fuzzy logic and demonstrated the great interest in controlling mobile robot using type-2 fuzzy logic. We deal with the design of new controllers for mobile robots using type-2 fuzzy logic in the navigation process in unknown and dynamic environments. The dynamicity of the environment is depicted by the presence of other dynamic robots. The performances of the proposed controllers are represented by both simulations and experimental results, and discussed over graphical paths and numerical analysis.

Cite this paper

N. Baklouti, R. John and A. Alimi, "Interval Type-2 Fuzzy Logic Control of Mobile Robots,"*Journal of Intelligent Learning Systems and Applications*, Vol. 4 No. 4, 2012, pp. 291-302. doi: 10.4236/jilsa.2012.44031.

N. Baklouti, R. John and A. Alimi, "Interval Type-2 Fuzzy Logic Control of Mobile Robots,"

References

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[2] B.-Y. Chee, S. Lang and P. Tse, “Fuzzy Mobile Robot Navigation and Sensor Integration,” Proceedings of the Fifth IEEE International Conference on Fuzzy Systems, Vol. 1, 1996, pp. 7-12. doi:10.1109/FUZZY.1996.551711

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[9] R. I. John, P. R. Innocent and M. R. Barnes, “NeuroFuzzy Clustering of Radiographic Tibia Image Data Using Type 2 Fuzzy Sets,” Information Sciences, Vol. 125, No. 1-4, 2000, pp. 65-82. doi:10.1016/S0020-0255(00)00009-8

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[12] Q. Liang and J. Mendel, “Equalization of Nonlinear Time-Varying Channels Using Type-2 Fuzzy Adaptive Filters,” IEEE Transactions on Fuzzy Systems, Vol. 8, No. 5, 2000, pp. 551-563. doi:10.1109/91.873578

[13] Q. Liang and J. M. Mendel, “Modeling mpeg vbr Video Traffic Using Type-2 Fuzzy Logic Systems,” In: Granular Computing: An Emerging Paradigm, Springer-Verlag, Berlin, 2001, pp. 367-383.

[14] C. Lynch, H. Hagras and V. Callaghan, “Embedded Type-2 FLC for Real-Time Speed Control of Marine and Traction Diesel Engines,” The 14th IEEE International Conference on Fuzzy Systems, Reno, 22-25 May 2005, pp. 347-352

[15] D. Wu and W. Tan, “A Type-2 Fuzzy Logic Controller for the Liquid-Level Process,” IEEE Proceedings of In- ternational Conference on Fuzzy Systems, Vol. 2, 2004, pp. 953-958.

[16] T. Dereli, A. Baykasoglu, K. Altun, A. Durmusoglu and I. Türksen, “Review: Industrial Applications of Type-2 Fuzzy Sets and Systems: A Concise Review,” Computers in Industy, Vol. 62, No. 2, 2011, pp 125-137. doi:10.1016/j.compind.2010.10.006

[17] L. A. Zadeh, “The Concept of a Linguistic Variable and Its Application to Approximate Reasoning-i,” Information Sciences, Vol. 8, No. 3, 1975, pp. 199-249. doi:10.1016/0020-0255(75)90036-5

[18] N. N. Karnik and J. M. Mendel, “Operations on Type-2 Fuzzy Sets,” Fuzzy Sets and Systems, Vol. 122, No. 2, 2001, pp. 327-348. doi:10.1016/S0165-0114(00)00079-8

[19] N. N. Karnik and J. M. Mendel, “Centroid of a Type-2 Fuzzy Set,” Information Sciences, Vol. 132, No. 1-4, 2001, pp. 195-220. doi:10.1016/S0020-0255(01)00069-X

[20] J. M. Mendel, R. I. John and F. Liu, “Interval Type-2 Fuzzy Logic Systems Made Simple,” IEEE Transactions on Fuzzy Systems, Vol. 14, No. 6, 2006, pp. 808-821. doi:10.1109/TFUZZ.2006.879986

[21] H. Hagras, “Type-2 FLCs: A New Generation of Fuzzy Controllers,” IEEE Computational Intelligence Magazine, Vol. 2, No. 1, 2007, pp. 30-43. doi:10.1109/MCI.2007.357192

[22] R. John and S. Coupland, “Type-2 Fuzzy Logic: A Historical View,” IEEE Computational Intelligence Magazine, Vol. 2, No. 1, 2007, pp. 57-62. doi:10.1109/MCI.2007.357194

[23] S. Coupland and R. John, “New Geometric Inference Techniques for Type-2 Fuzzy Sets,” International Journal of Approximate Reasoning, Vol. 49, No. 1, 2008, pp. 198-211. doi:10.1016/j.ijar.2008.03.001

[24] N. Baklouti and A. M. Alimi, “The Geometric Interval Type-2 Fuzzy Logic Approach in Robotic Mobile Issue,” IEEE International Conference on Fuzzy Systems, Jeju Island, 20-24 August 2009, pp. 1971-1976. doi:10.1109/FUZZY.2009.5277307

[25] H. Hagras, “A Hierarchical Type-2 Fuzzy Logic Control Architecture for Autonomous Mobile Robots,” IEEE Transactions on Fuzzy Systems, Vol. 12, No. 4, 2004, pp. 524-539. doi:10.1109/TFUZZ.2004.832538

[26] N. Baklouti and A. M. Alimi, “Motion Planning in Dynamic and Unknown Environment Using an Interval Type-2 tsk Fuzzy Logic Controller,” IEEE International Conference on Fuzzy Systems, London, 23-26 July 2007, pp. 1-6. doi:10.1109/FUZZY.2007.4295647

[27] J. Figueroa, J. Posada, J. Soriano, M. Melgarejo and S. Rojas, “A Type-2 Fuzzy Controller for Tracking Mobile Objects in the Context of Robotic Soccer Games,” The 14th IEEE International Conference on Fuzzy Systems, Reno, 22-25 May 2005, pp. 359-364.

[28] P. Phokharatkul and S. Phaiboon, “Mobile Robot Control Using Type-2 Fuzzy Logic System,” 2004 IEEE Conference on Robotics, Automation and Mechatronics, Vol. 1, 2004, pp. 296-299.

[29] K. C. Wu, “Fuzzy Interval Control of Mobile Robots,” Computers and Electrical Engineering, Vol. 22, No. 3, 1996, pp. 211-229. doi:10.1016/0045-7906(95)00038-0

[30] KTEAM, 2007. http://www.k-team.com

[31] T. Takagi and M. Sugeno, “Fuzzy Identification of Systems and Its Applications to Modeling and Control,” IEEE Transactions on Systems, Man, and Cybernetics, Vol. 15, No. 1, 1985, pp. 116-132. doi:10.1109/TSMC.1985.6313399

[32] Q. Liang and J. Mendel, “An Introduction to Type-2 tsk Fuzzy Logic Systems,” 1999 IEEE International Conference Fuzzy Systems, Vol. 3, 1999, pp. 1534-1539.

[33] Autonomous Mobile Robotics Toolbox SIMROBOT. http://www.uamt. feec.vutbr.cz/robotics/simulations /amrt/simrobot.zip

[34] G. Benet, F. Blanes, J. E. Simу and P. Pйrez, “Using Infrared Sensors for Distance Measurement in Mobile Robots,” Robotics and Autonomous Systems, Vol. 40, No. 4, 2002, pp. 255-266.

[1] J. Borenstein, “Experimental Results from Internal Odometry Error Correction with the Omnimate Mobile Robot,” IEEE Transactions on Robotics and Automation, Vol. 14, No. 6, 1998, pp. 963-969. doi:10.1109/70.736779

[2] B.-Y. Chee, S. Lang and P. Tse, “Fuzzy Mobile Robot Navigation and Sensor Integration,” Proceedings of the Fifth IEEE International Conference on Fuzzy Systems, Vol. 1, 1996, pp. 7-12. doi:10.1109/FUZZY.1996.551711

[3] A. Ramirez-Serrano and M. Boumedine, “Real-Time Navigation in Unknown Environments Using Fuzzy Logic and Ultrasonic Sensing,” Dearborn, 15-18 September 1996, pp. 26-30.

[4] D. Springer, J. Zhang and A. Knoll, “Integrating Deliberative and Reactive Strategies via Fuzzy Modular Control,” 1999. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.44.5267

[5] J. Zhang, F. Wille and A. Knoll, “Fuzzy Logic Rules for Mapping Sensor Data to Robot Control,” Proceedings of the First Euromicro Workshop on Advanced Mobile Robot, Vol. 10, No. 2, 1996, pp. 29-38. doi:10.1109/EURBOT.1996.551878

[6] J. Mendel and R. John, “Type-2 Fuzzy Sets Made Simple,” IEEE Transactions on Fuzzy Systems, Vol. 10, No. 2, 2002, pp. 117-127. doi:10.1109/91.995115

[7] N. Karnik, J. Mendel and Q. Liang, “Type-2 Fuzzy Logic Systems,” IEEE Transactions on Fuzzy Systems, Vol. 7, No. 6, 1999, pp. 643-658. doi:10.1109/91.811231

[8] J. Mendel and R. John, “A Fundamental Decomposition of Type-2 Fuzzy Sets,” IFSA World Congress and 20th NAFIPS International Conference, Vol. 4, 2001, pp. 1896-1901.

[9] R. I. John, P. R. Innocent and M. R. Barnes, “NeuroFuzzy Clustering of Radiographic Tibia Image Data Using Type 2 Fuzzy Sets,” Information Sciences, Vol. 125, No. 1-4, 2000, pp. 65-82. doi:10.1016/S0020-0255(00)00009-8

[10] R. John and S. Lake, “Type-2 Fuzzy Sets for Modelling Nursing Intuition,” IFSA World Congress and 20th NAFIPS International Conference, Vol. 4, 2001, pp. 1920-1925.

[11] N. N. Karnik and J. M. Mendel, “Applications of Type-2 Fuzzy Logic Systems to Forecasting of Time-Series,” Information Sciences, Vol. 120, No. 1-4, 1999, pp. 89-111. doi:10.1016/S0020-0255(99)00067-5

[12] Q. Liang and J. Mendel, “Equalization of Nonlinear Time-Varying Channels Using Type-2 Fuzzy Adaptive Filters,” IEEE Transactions on Fuzzy Systems, Vol. 8, No. 5, 2000, pp. 551-563. doi:10.1109/91.873578

[13] Q. Liang and J. M. Mendel, “Modeling mpeg vbr Video Traffic Using Type-2 Fuzzy Logic Systems,” In: Granular Computing: An Emerging Paradigm, Springer-Verlag, Berlin, 2001, pp. 367-383.

[14] C. Lynch, H. Hagras and V. Callaghan, “Embedded Type-2 FLC for Real-Time Speed Control of Marine and Traction Diesel Engines,” The 14th IEEE International Conference on Fuzzy Systems, Reno, 22-25 May 2005, pp. 347-352

[15] D. Wu and W. Tan, “A Type-2 Fuzzy Logic Controller for the Liquid-Level Process,” IEEE Proceedings of In- ternational Conference on Fuzzy Systems, Vol. 2, 2004, pp. 953-958.

[16] T. Dereli, A. Baykasoglu, K. Altun, A. Durmusoglu and I. Türksen, “Review: Industrial Applications of Type-2 Fuzzy Sets and Systems: A Concise Review,” Computers in Industy, Vol. 62, No. 2, 2011, pp 125-137. doi:10.1016/j.compind.2010.10.006

[17] L. A. Zadeh, “The Concept of a Linguistic Variable and Its Application to Approximate Reasoning-i,” Information Sciences, Vol. 8, No. 3, 1975, pp. 199-249. doi:10.1016/0020-0255(75)90036-5

[18] N. N. Karnik and J. M. Mendel, “Operations on Type-2 Fuzzy Sets,” Fuzzy Sets and Systems, Vol. 122, No. 2, 2001, pp. 327-348. doi:10.1016/S0165-0114(00)00079-8

[19] N. N. Karnik and J. M. Mendel, “Centroid of a Type-2 Fuzzy Set,” Information Sciences, Vol. 132, No. 1-4, 2001, pp. 195-220. doi:10.1016/S0020-0255(01)00069-X

[20] J. M. Mendel, R. I. John and F. Liu, “Interval Type-2 Fuzzy Logic Systems Made Simple,” IEEE Transactions on Fuzzy Systems, Vol. 14, No. 6, 2006, pp. 808-821. doi:10.1109/TFUZZ.2006.879986

[21] H. Hagras, “Type-2 FLCs: A New Generation of Fuzzy Controllers,” IEEE Computational Intelligence Magazine, Vol. 2, No. 1, 2007, pp. 30-43. doi:10.1109/MCI.2007.357192

[22] R. John and S. Coupland, “Type-2 Fuzzy Logic: A Historical View,” IEEE Computational Intelligence Magazine, Vol. 2, No. 1, 2007, pp. 57-62. doi:10.1109/MCI.2007.357194

[23] S. Coupland and R. John, “New Geometric Inference Techniques for Type-2 Fuzzy Sets,” International Journal of Approximate Reasoning, Vol. 49, No. 1, 2008, pp. 198-211. doi:10.1016/j.ijar.2008.03.001

[24] N. Baklouti and A. M. Alimi, “The Geometric Interval Type-2 Fuzzy Logic Approach in Robotic Mobile Issue,” IEEE International Conference on Fuzzy Systems, Jeju Island, 20-24 August 2009, pp. 1971-1976. doi:10.1109/FUZZY.2009.5277307

[25] H. Hagras, “A Hierarchical Type-2 Fuzzy Logic Control Architecture for Autonomous Mobile Robots,” IEEE Transactions on Fuzzy Systems, Vol. 12, No. 4, 2004, pp. 524-539. doi:10.1109/TFUZZ.2004.832538

[26] N. Baklouti and A. M. Alimi, “Motion Planning in Dynamic and Unknown Environment Using an Interval Type-2 tsk Fuzzy Logic Controller,” IEEE International Conference on Fuzzy Systems, London, 23-26 July 2007, pp. 1-6. doi:10.1109/FUZZY.2007.4295647

[27] J. Figueroa, J. Posada, J. Soriano, M. Melgarejo and S. Rojas, “A Type-2 Fuzzy Controller for Tracking Mobile Objects in the Context of Robotic Soccer Games,” The 14th IEEE International Conference on Fuzzy Systems, Reno, 22-25 May 2005, pp. 359-364.

[28] P. Phokharatkul and S. Phaiboon, “Mobile Robot Control Using Type-2 Fuzzy Logic System,” 2004 IEEE Conference on Robotics, Automation and Mechatronics, Vol. 1, 2004, pp. 296-299.

[29] K. C. Wu, “Fuzzy Interval Control of Mobile Robots,” Computers and Electrical Engineering, Vol. 22, No. 3, 1996, pp. 211-229. doi:10.1016/0045-7906(95)00038-0

[30] KTEAM, 2007. http://www.k-team.com

[31] T. Takagi and M. Sugeno, “Fuzzy Identification of Systems and Its Applications to Modeling and Control,” IEEE Transactions on Systems, Man, and Cybernetics, Vol. 15, No. 1, 1985, pp. 116-132. doi:10.1109/TSMC.1985.6313399

[32] Q. Liang and J. Mendel, “An Introduction to Type-2 tsk Fuzzy Logic Systems,” 1999 IEEE International Conference Fuzzy Systems, Vol. 3, 1999, pp. 1534-1539.

[33] Autonomous Mobile Robotics Toolbox SIMROBOT. http://www.uamt. feec.vutbr.cz/robotics/simulations /amrt/simrobot.zip

[34] G. Benet, F. Blanes, J. E. Simу and P. Pйrez, “Using Infrared Sensors for Distance Measurement in Mobile Robots,” Robotics and Autonomous Systems, Vol. 40, No. 4, 2002, pp. 255-266.