Robot navigation with obstacle avoidance in unknown environment

  • Abstract
  • Keywords
  • References
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  • 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.

  • Keywords

    ANFIS; fuzzy controller; obstacle avoidance; path planning; robot navigation.

  • References

      [1] A. Narayan, E. Tuci, F. Labrosse, and M. H. M. Alkilabi, “A dynamic colour perception system for autonomous robot navigation on unmarked roads,” Neurocomputing, vol. 275, pp. 2251 – 2263, 2018. [Online]. Available:

      [2] N. Kumar and Z. Vámossy, “Obstacle recognition and avoidance during robot navigation in unknown environment,” International Journal of Engineering & Technology, vol. 7, no. 3, pp. 1400 – 1404, 2018. [Online]. Available:

      [3] G. Kertész, S. Szénási, and Z. Vámossy, “Multi-directional image projections with fixed resolution for object matching,” Acta Polytechnica Hungarica, vol. 15, no. 2, pp. 211 – 229, 2018. [Online]. Available: Szenasi Vamossy 81.pdf

      [4] I. Arvanitakis, A. Tzes, and K. Giannousakis, “Mobile robot navigation under pose uncertainty in unknown environments.” IFAC-PapersOnLine, vol. 50, no. 1, pp. 12 710 – 12 714, 2017, 20th IFAC World Congress. [Online]. Available: http: // /article/pii/ S2405896317330628

      [5] I. Rodrı́guez-Fdez, M. Mucientes, and A. Bugarı́n, “Learning fuzzy controllers in mobile robotics with embedded preprocessing,” Applied Soft Computing, vol. 26, pp. 123 – 142, 2015. [Online]. Available: http: //

      [6] N. H. Singh and K. Thongam, “Mobile robot navigation using fuzzy logic in static environments,” Procedia Computer Science, vol. 125, pp. 11 – 17, 2018, the 6th International Conference on Smart Computing and Communications. [Online]. Available: http://www

      [7] T. T. Mac, C. Copot, D. T. Tran, and R. D. Keyser, “Heuristic approaches in robot path planning: A survey,” Robotics and Autonomous Systems, vol. 86, pp. 13 – 28, 2016. [Online]. Available: http:


      [8] M. Algabri, H. Mathkour, H. Ramdane, and M. Alsulaiman, “Comparative study of soft computing techniques for mobile robot navigation in an unknown environment,” Computers in Human Behavior, vol. 50, pp. 42 – 56, 2015. [Online]. Available: http: // /science/article/pii/S0747563215002605

      [9] M. S. Masmoudi, N. Krichen, M. Masmoudi, and N. Derbel, “Fuzzy logic controllers design for omnidirectional mobile robot navigation,” Applied Soft Computing, vol. 49, pp. 901 – 919, 2016.[Online]. Available: S1568494616304598

      [10] N. Kumar, M. Takács, and Z. Vámossy, “Robot navigation in unknown environment using fuzzy logic,” in 2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI), Jan 2017, pp. 279 – 284. [Online]. Available: /document/7880317/

      [11] R. C. Coulter, “Implementation of the pure pursuit path tracking algorithm,” Carnegie Mellon University, Pittsburgh, PA, Tech. Rep. CMU-RI-TR-92-01, January 1992. [Online]. Available:




Article ID: 14767
DOI: 10.14419/ijet.v7i4.14767

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