Advertisement

Towards a Control Strategy Based on Type-2 Fuzzy Logic for an Autonomous Mobile Robot

  • Felizardo Cuevas
  • Oscar CastilloEmail author
  • Prometeo Cortes
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 827)

Abstract

The main purpose considered in this paper is to maintain a specific location and behavior for a robot that uses type-2 fuzzy logic for controlling its behavior. In this work, we propose a combination of behaviors by following a trajectory without leaving or losing it and avoiding obstacles in an omnidirectional mobile platform. The results of the simulation show the advantages of the proposed approach. We describe the previous knowledge about type-2 fuzzy logic, the virtualization of the mobile robot and its modeling according to real situations. The proposed control system is developed in Matlab-Simulink, the system can model and guide a mobile robot, successfully in simulated and real environments.

Keywords

AMR (Autonomous mobile robots) T2FS (Type-2 fuzzy systems) T2FLC (Type-2 fuzzy logic controller) OMR (Omnidirectional movil robot) 

References

  1. 1.
    S. Oltean, M. Dulau, Position control of Robotino mobile robot using fuzzy logic, in 2010 IEEE International Conference on Automation, Quality and Testing, Robotics (2010), pp. 1–6Google Scholar
  2. 2.
    T.T. Mac, C. Copot, R.D.E. Keyser, T.D. Tran, T. Vu, MIMO fuzzy control for autonomous mobile robot. J. Autom. Control Eng. 4, 65–70 (2016)Google Scholar
  3. 3.
    F. Cuevas, O. Castillo, Design and implementation of a fuzzy path optimization system for omnidirectional autonomous mobile robot control in real-time, in Studies in computational intelligence, vol. 749 (2018)Google Scholar
  4. 4.
    A.P. Moon, K.K Jajuiwar, Design of adaptive fuzzy tracking controller for autonomous navigation system. Int. J. Recent Trend Eng. Res. 2, 268–275 (2016) (ijrter.com)Google Scholar
  5. 5.
    X. Li, A. Zell, Motion control of an omnidirectional mobile robot, in Informatics in Control, Automation and Robotics (Springer Berlin Heidelberg, Berlin, Heidelberg), pp. 181–193Google Scholar
  6. 6.
    D.R. Parhi, B.B.V.L. Deepak, Kinematic model of three wheeled mobile robot. Mech. Eng. Res. 3, 307–318 (2011)Google Scholar
  7. 7.
    Y. Zhao, S.L. BeMent, Kinematics, dynamics and control of wheeled mobile robots, in Proceedings of 1992 IEEE International Conference on Robotics and Automation (1992), pp 91–96Google Scholar
  8. 8.
    D. Garcia Sillas, E. Gorrostieta Hurtado, E. Vargas Soto, J. Rodríguez Reséndiz, S. Tovar Arriaga, Kinematics modeling and simulation of an autonomous omni-directional mobile robot. Ing. e Investig. 35, 74–79 (2015)CrossRefGoogle Scholar
  9. 9.
    J.B. Song, K.S. Byun, Design and control of an omnidirectional mobile robot with steerable omnidirectional wheels, in Mobile Robots, Moving Intelligence (2006), pp. 224–240Google Scholar
  10. 10.
    K. Kanjanawanishkul, Omnidirectional wheeled mobile robots: wheel types and practical applications. Int. J. Adv. Mechatron. Syst. 6, 289 (2015)CrossRefGoogle Scholar
  11. 11.
    M. Njah, (ICCAT, M.J.-C.A.T., Wheelchair obstacle avoidance based on fuzzy controller and ultrasonic sensors, in International Conference on Computer Applications Technology (ICCAT) (2013), pp. 1–5Google Scholar
  12. 12.
    O. Castillo, P. Melin, A review on the design and optimization of interval type-2 fuzzy controllers. Appl. Soft Comput. J. 12, 1267–1278 (2012)CrossRefGoogle Scholar
  13. 13.
    O. Castillo, P. Melin, W. Pedrycz, Design of interval type-2 fuzzy models through optimal granularity allocation. Appl. Soft Comput. J. 11, 5590–5601 (2011)CrossRefGoogle Scholar
  14. 14.
    D. Wu, On the fundamental differences between interval Type-2 and Type-1 fuzzy logic controllers. IEEE Trans. Fuzzy Syst. 20(5), 832–848 (2012)CrossRefGoogle Scholar
  15. 15.
    Festo Didactic GmbH & Co. KG, in Robotino Manual (Denkendorf, 2007), pp. 7–25Google Scholar
  16. 16.
    P. Ochoa, O. Castillo, J. Soria, Differential Evolution with Dynamic Adaptation of Parameters for the Optimization of Fuzzy Controllers. Presented at the (2014)Google Scholar
  17. 17.
    L. Rodríguez, O. Castillo, J. Soria, P. Melin, F. Valdez, C.I. Gonzalez, G.E. Martinez, J. Soto, A fuzzy hierarchical operator in the grey wolf optimizer algorithm. Appl. Soft Comput. J. 57, 315–328 (2017)CrossRefGoogle Scholar
  18. 18.
    R. Martínez-Soto, O. Castillo, L.T. Aguilar, A. Rodriguez, A hybrid optimization method with PSO and GA to automatically design Type-1 and Type-2 fuzzy logic controllers. Int. J. Mach. Learn. Cybern. 6(2), 175–196 (2015)CrossRefGoogle Scholar
  19. 19.
    L. Amador-Angulo, O. Castillo, Optimization of the Type-1 and Type-2 fuzzy controller design for the water tank using the Bee Colony Optimization, in 2014 IEEE Conference on Norbert Wiener in the 21st Century (21CW) (IEEE, 2014), pp. 1–8Google Scholar
  20. 20.
    D.V. Bhoyar, P.B.J. Chilke, S.S. Kemekar, in Design and Analysis of fuzzy PID Controllers using Genetic Algorithm (2016), pp. 135–138Google Scholar
  21. 21.
    C. Caraveo, F. Valdez, O. Castillo, Optimization of fuzzy controller design using a new bee colony algorithm with fuzzy dynamic parameter adaptation. Appl. Soft Comput. 43, 131–142 (2016)CrossRefGoogle Scholar
  22. 22.
    J. Pérez, F. Valdez, O. Castillo, Modification of the bat algorithm using type-2 fuzzy logic for dynamical parameter adaptation, in Studies in Computational Intelligence (2017)Google Scholar
  23. 23.
    O. Castillo, R. Martínez-Marroquín, P. Melin, F. Valdez, J. Soria, Comparative study of bio-inspired algorithms applied to the optimization of type-1 and type-2 fuzzy controllers for an autonomous mobile robot. Inf. Sci. (Ny) 192, 19–38 (2012)CrossRefGoogle Scholar
  24. 24.
    L. Amador-Angulo, O. Mendoza, J. Castro, A. Rodríguez-Díaz, P. Melin, O. Castillo, Fuzzy sets in dynamic adaptation of parameters of a bee colony optimization for controlling the trajectory of an autonomous mobile robot. Sensors 16, 1458 (2016)CrossRefGoogle Scholar
  25. 25.
    P. Melin, L. Astudillo, O. Castillo, F. Valdez, M. Garcia, Optimal design of type-2 and type-1 fuzzy tracking controllers for autonomous mobile robots under perturbed torques using a new chemical optimization paradigm. Expert Syst. Appl. 40, 3185–3195 (2013)CrossRefGoogle Scholar
  26. 26.
    Patricia Melin, Claudia I. González, Juan R. Castro, Olivia Mendoza, Oscar Castillo, Edge-Detection method for image processing based on generalized Type-2 fuzzy logic. IEEE Trans. Fuzzy Syst. 22(6), 1515–1525 (2014)CrossRefGoogle Scholar
  27. 27.
    Claudia I. González, Patricia Melin, Juan R. Castro, Oscar Castillo, Olivia Mendoza, Optimization of interval type-2 fuzzy systems for image edge detection. Appl. Soft Comput. 47, 631–643 (2016)CrossRefGoogle Scholar
  28. 28.
    Claudia I. González, Patricia Melin, Juan R. Castro, Olivia Mendoza, Oscar Castillo, An improved sobel edge detection method based on generalized type-2 fuzzy logic. Soft. Comput. 20(2), 773–784 (2016)CrossRefGoogle Scholar
  29. 29.
    Emanuel Ontiveros, Patricia Melin, Oscar Castillo, High order α-planes integration: a new approach to computational cost reduction of General Type-2 fuzzy systems. Eng. Appl. of AI 74, 186–197 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Felizardo Cuevas
    • 1
  • Oscar Castillo
    • 1
    Email author
  • Prometeo Cortes
    • 1
  1. 1.Tijuana Institute of TechnologyTijuanaMexico

Personalised recommendations