Robot navigation with obstacle avoidance in unknown environment

  • Authors

    • Neerendra Kumar John von Neumann Faculty of Informatics, Óbuda University, Budapest, Hungary.
    • Zoltán Vámossy John von Neumann Faculty of Informatics, Óbuda University, Budapest, Hungary.
    2018-09-17
    https://doi.org/10.14419/ijet.v7i4.14767
  • ANFIS, fuzzy controller, obstacle avoidance, path planning, robot navigation.

  • Abstract

    In this paper, a robot navigation model is constructed in MATLAB-Simulink. This robot navigation model make the robot capable for the obstacles avoidance in unknown environment. The navigation model uses two types of controllers: pure pursuit controller and fuzzy logic controller. The role of the pure pursuit controller is to generate linear and angular velocities to drive the robot from its current position to the given goal position. The obstacle avoidance is achieved through the fuzzy logic controller. For the fuzzy controller, two novel fuzzy inference systems (FISs) are developed. Initially, a Mamdani-type fuzzy inference system (FIS) is generated. Using this Mamdani-type FIS in the fuzzy controller, the training data of input and output mapping, is collected. This training data is supplied to the adaptive neuro-fuzzy inference system (ANFIS) to obtain the second FIS as of Sugeno-type. The navigation model, using the proposed FISs, is implemented on the simulated as well as real robots.

  • References

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  • How to Cite

    Kumar, N., & Vámossy, Z. (2018). Robot navigation with obstacle avoidance in unknown environment. International Journal of Engineering & Technology, 7(4), 2410-2417. https://doi.org/10.14419/ijet.v7i4.14767

    Received date: 2018-06-29

    Accepted date: 2018-08-30

    Published date: 2018-09-17