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Omnidirectional Four Wheel Mobile Robot Control with a Type-2 Fuzzy Logic Behavior-Based Strategy

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Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 862))

Abstract

The main goal considered in this paper is to maintain a specific location and behavior for a robot by using type-2 fuzzy logic for controlling its behavior. We describe the basic concepts about type-2 fuzzy logic, the virtualization of the mobile robot and its modeling according to real situations. Kinematic equations and fuzzy logic are used to control the motor speed of the wheels. The proposed control system is developed in Matlab-Simulink, the system can model and guide a mobile robot, successfully in simulated and real environments. Comparisons of the system performance are made by changing the number of the membership functions in the Type-2 Fuzzy Logic Controller of the robot. The results of the simulation show the advantages of the proposed approach. Also a Mecanum-wheeled robot benefits from great omni-direction maneuverability.

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Authors and Affiliations

  1. Tijuana Institute of Technology, Tijuana, BC, Mexico

    Felizardo Cuevas, Oscar Castillo & Prometeo Cortés-Antonio

Authors
  1. Felizardo Cuevas
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  2. Oscar Castillo
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  3. Prometeo Cortés-Antonio
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Corresponding author

Correspondence to Oscar Castillo .

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Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Oscar Castillo

  2. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Patricia Melin

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

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Cuevas, F., Castillo, O., Cortés-Antonio, P. (2020). Omnidirectional Four Wheel Mobile Robot Control with a Type-2 Fuzzy Logic Behavior-Based Strategy. In: Castillo, O., Melin, P., Kacprzyk, J. (eds) Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications. Studies in Computational Intelligence, vol 862. Springer, Cham. https://doi.org/10.1007/978-3-030-35445-9_4

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