Implementation of a Fuzzy Controller for an Autonomous Mobile Robot in the PIC18F4550 Microcontroller

  • Oscar Carvajal
  • Oscar CastilloEmail author
Part of the Studies in Computational Intelligence book series (SCI, volume 827)


Soft Computing has been gaining popularity in real world applications in many fields, an example of the area that has a wide variety of applications of these techniques is the Robotics area. In this work, we introduce the design of a hardware system for an autonomous mobile robot and the development of a Fuzzy Logic Controller for the control of the motion of a robot to follow a trajectory. We consider the error in the distance to the path as the unique input to the fuzzy controller and as the outputs, the linear velocity of each of the two wheels of the robot. We also show the development of the firmware in the PIC18F4550 microcontroller as the implementation of the Fuzzy Logic Controller.


Fuzzy logic Microcontroller Firmware Robotics 


  1. 1.
    C. Rekik, M. Jallouli, N. Derbel, Optimal trajectory of a mobile robot using hierarchical fuzzy logic controller. Int. J. Comput. Appl. Technol. 53(4), 348–357 (2016)CrossRefGoogle Scholar
  2. 2.
    O. Carvajal, O. Castillo, J. Soria, Optimization of membership function parameters for fuzzy controllers of an autonomous mobile robot using the flower pollination algorithm. J. Autom. Mob. Robot. Intell. Syst. 12(1), 44–49 (2018)Google Scholar
  3. 3.
    A. Hechri, A. Ladgham, F. Hamdaoui, A. Mtibaa, Design of fuzzy logic controller for autonomous parking of mobile robot. Int. J. Sci. Tech. Autom. Control Comput. Eng. 5(2), 1558–1575 (2011)Google Scholar
  4. 4.
    H. Erdem, Application of neuro-fuzzy controller for sumo robot control. Expert Syst. Appl. 38(8), 9752–9760 (2011)CrossRefGoogle Scholar
  5. 5.
    G. Dudek, M. Jenkin, Computational principles of mobile robotics (Cambridge University Press, Cambridge, 2010)CrossRefGoogle Scholar
  6. 6.
    L.A. Zadeh, A rationale for fuzzy control. J. Dyn. Syst. Meas. Control 94(1), 3–4 (1972)MathSciNetCrossRefGoogle Scholar
  7. 7.
    L.A. Zadeh, Toward a generalized theory of uncertainty (GTU)—an outline. Inf. Sci. (Ny) 172(1–2), 1–40 (2005)MathSciNetCrossRefGoogle Scholar
  8. 8.
    E. Lizarraga, O. Castillo, J. Soria, F. Valdez, A fuzzy control design for an autonomous mobile robot using ant colony optimization, in Recent Advances on Hybrid Approaches for Designing Intelligent Systems (Springer, Berlin), pp. 289–304Google Scholar
  9. 9.
    L. Amador-Angulo, O. Castillo, J.R. Castro, A generalized type-2 fuzzy logic system for the dynamic adaptation the parameters in a Bee Colony Optimization algorithm applied in an autonomous mobile robot control, in 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (2016), pp. 537–544Google Scholar
  10. 10.
    P. Glotfelter, M. Egerstedt, A parametric MPC approach to balancing the cost of abstraction for differential-drive mobile robots. arXiv Prepr. arXiv1802.07199 (2018)Google Scholar
  11. 11.
    M. Egerstedt, X. Hu, H. Rehbinder, A. Stotsky, Path planning and robust tracking for a car-like robot, in Proceedings of the 5th Symposium on Intelligent Robotic Systems (1997), pp. 237–243Google Scholar
  12. 12.
    V.M. Peri, D. Simon, Fuzzy logic control for an autonomous robot, in NAFIPS 2005. Annual Meeting of the North American Fuzzy Information Processing Society (2005), pp. 337–342Google Scholar
  13. 13.
    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(8), 3185–3195 (2013)CrossRefGoogle Scholar
  14. 14.
    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
  15. 15.
    C.T. Kilian, Modern Control Technology: Components and Systems (Delmar/Thomson Learning, 2006)Google Scholar
  16. 16.
    J. Perez, P. Melin, O. Castillo, F. Valdez, C. Gonzalez, G. Martinez, Trajectory optimization for an autonomous mobile robot using the bat algorithm, in North American Fuzzy Information Processing Society Annual Conference (2017), pp. 232–241Google Scholar
  17. 17.
    O. Castillo, J. Soria, J. Kacprzyk, Optimization of reactive control for mobile robots based on the CRA using type-2 fuzzy logic, in Nature-Inspired Design of Hybrid Intelligent Systems (Springer, 2017), pp. 505–515Google Scholar
  18. 18.
    P. Melin, O. Castillo, Fuzzy controllers for autonomous mobile robots, in Springer Handbook of Computational Intelligence (Springer, 2015), pp. 1517–1531Google Scholar
  19. 19.
    M.A. Sanchez, O. Castillo, J.R. Castro, Generalized type-2 fuzzy systems for controlling a mobile robot and a performance comparison with interval type-2 and type-1 fuzzy systems. Expert Syst. Appl. 42(14), 5904–5914 (2015)CrossRefGoogle Scholar
  20. 20.
    O. Castillo, H. Neyoy, J. Soria, P. Melin, F. Valdez, A new approach for dynamic fuzzy logic parameter tuning in ant colony optimization and its application in fuzzy control of a mobile robot. Appl. Soft Comput. 28, 150–159 (2015)CrossRefGoogle Scholar
  21. 21.
    C. Leal Ramírez, O. Castillo, P. Melin, A. Rodríguez Díaz, Simulation of the bird age-structured population growth based on an interval type-2 fuzzy cellular structure. Inf. Sci. 181(3), 519–535 (2011)MathSciNetCrossRefGoogle Scholar
  22. 22.
    N.R. Cázarez-Castro, L.T. Aguilar, O. Castillo, Designing Type-1 and Type-2 fuzzy logic controllers via fuzzy lyapunov synthesis for nonsmooth mechanical systems. Eng. Appl. AI 25(5), 971–979 (2012)CrossRefGoogle Scholar
  23. 23.
    O. Castillo, P. Melin, Intelligent systems with interval type-2 fuzzy logic. Int. J. Innov. Comput. Inf. Control 4(4), 771–783 (2008)Google Scholar
  24. 24.
    G.M. Mendez, O. Castillo, Interval type-2 TSK fuzzy logic systems using hybrid learning algorithm, in The 14th IEEE International Conference on Fuzzy Systems. FUZZ’05 (2005), pp. 230–235Google Scholar
  25. 25.
    P. Melin, C.I. González, J.R. Castro, O. Mendoza, O. 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
  26. 26.
    L. Cervantes, O. Castillo, D. Hidalgo, R. Martinez-soto, Fuzzy dynamic adaptation of gap generation and mutation in genetic optimization of type 2 fuzzy controllers, in Advances in Operation Research, vol. 2018 (Hindai, 2018)CrossRefGoogle Scholar
  27. 27.
    P. Melin, O. Castillo, Intelligent control of complex electrochemical systems with a neuro-fuzzy-genetic approach. IEEE Trans. Ind. Electron. 48(5), 951–955 (2001)CrossRefGoogle Scholar
  28. 28.
    E. Rubio, O. Castillo, F. Valdez, P. Melin, C. I. González, G. Martinez. An extension of the fuzzy possibilistic clustering algorithm using type-2 fuzzy logic techniques. Adv. Fuzzy Syst. 2017, 7094046:1–7094046:23 (2017)CrossRefGoogle Scholar
  29. 29.
    P. Melin, A. Mancilla, M. Lopez, O. Mendoza, A hybrid modular neural network architecture with fuzzy Sugeno integration for time series forecasting. Appl. Soft Comput. 7(4), 1217–1226 (2007)CrossRefGoogle Scholar
  30. 30.
    P. Melin, O. Castillo. Modelling, Simulation and Control of Non-Linear Dynamical Systems: An Intelligent Approach Using Soft Computing and Fractal Theory (CRC Press, 2001)Google Scholar
  31. 31.
    P. Melin, G. Prado-Arechiga, New Hybrid Intelligent Systems for Diagnosis and Risk Evaluation of Arterial Hypertension (Springer, Switzerland, 2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Tijuana Institute of TechnologyTijuanaMexico

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