Automatic Control and Computer Sciences

, Volume 52, Issue 5, pp 382–391 | Cite as

Design of Sensor Data Fusion Algorithm for Mobile Robot Navigation Using ANFIS and Its Analysis Across the Membership Functions

  • S. AdarshEmail author
  • K. I. Ramachandran


Design and development of autonomous mobile robots attracts more attention in the era of autonomous navigation. There are various algorithms used in practice for solving research problems related to the robot model and its operating environment. This paper presents the design of data fusion algorithm using Adaptive Neuro Fuzzy Interface (ANFIS) for the navigation of mobile robots. Detailed analysis of various membership functions (MFs) provided in this paper helps to select the most appropriate MF for the design of similar navigation systems. The combined use of fuzzy and neural networks in ANFIS makes the measured distance value of the residual covariance consistent with its actual value. The data fusion algorithm within the controller of the mobile robot fuses the input from ultrasonic and infrared sensors for better environment perception. The results indicate that the data fusion algorithm provides minimal root mean square error (RMSE) and mean absolute percentage error (MAPE) when compared with that of the individual sensors.


fuzzy ANFIS data fusion membership functions evaluation ultrasonic sensor IR sensor error analysis 


  1. 1.
    Abiyev, R., Ibrahim, D., and Erin, B., Advances in engineering software navigation of mobile robots in the presence of obstacles, Adv. Eng. Software, 2010, vol. 41, pp. 1179–1186.CrossRefzbMATHGoogle Scholar
  2. 2.
    Rusu, P., Petriu, E.M., Whalen, T.E., Cornell, A., and Spoelder, H.J.W., Behavior-based neuro-fuzzy controller for mobile robot navigation, IEEE Trans. Instrum. Meas., 2003, vol. 52, no. 4, pp. 1335–1340.CrossRefGoogle Scholar
  3. 3.
    Capi, G., Kaneko, S. and Hua, B., Neural network based guide robot navigation: An evolutionary approach, Procedia Comput. Sci., 2015, vol. 76, pp. 74–79.CrossRefGoogle Scholar
  4. 4.
    Faisal, M., Hedjar, R., Al Sulaiman, M., and Al-Mutib, Kh., Fuzzy logic navigation and obstacle avoidance by a mobile robot in an unknown dynamic environment, Int. J. Adv. Rob. Syst., 2013, vol. 10, no. 1.Google Scholar
  5. 5.
    Omrane, H., Masmoudi, M.S., and Masmoudi, M., Fuzzy logic based control for autonomous mobile robot navigation, Comput. Intell. Neurosci., 2016, vol. 2016.Google Scholar
  6. 6.
    Anish Pandey and Dayal R. Parhi, Optimum path planning of mobile robot in unknown static and dynamic environments using Fuzzy-Wind Driven Optimization algorithm, Def. Technol., 2017, vol. 13, no. 1, pp. 47–58.Google Scholar
  7. 7.
    Luo, R.C., Yih, C.C., and Su, K.L., Multisensor fusion and integration: Approaches, applications, and future research directions, IEEE Sens. J., 2002, vol. 2, no. 2, pp. 107–119.CrossRefGoogle Scholar
  8. 8.
    Wu, Y.-G., Yang, J.-Y., and Liu, K., Obstacle detection and environment modeling based on multisensor fusion for robot navigation, Artif. Intell. Eng., 1996, vol. 10, no. 4, pp. 323–333.CrossRefGoogle Scholar
  9. 9.
    Marwah Almasri, Khaled Elleithy, and Abrar Alajlan, Sensor fusion based model for collision free mobile robot navigation, Sensors, 2016, vol. 16, no. 1, p. 24.CrossRefGoogle Scholar
  10. 10.
    Mar, J. and Lin, F.J., An ANFIS controller for the car-following collision prevention system, IEEE Trans. Veh. Technol., 2001, vol. 50, no. 4, pp. 1106–1113.CrossRefGoogle Scholar
  11. 11.
    Bai, Y. and Wang, D., Fundamentals of fuzzy logic control—fuzzy sets, fuzzy rules and defuzzifications, in Advanced Fuzzy Logic Technologies in Industrial Applications. Advances in Industrial Control,Bai, Y., Zhuang, H., and Wang, D., Eds., London: Springer, 2006.CrossRefGoogle Scholar
  12. 12.
    Zhao, J. and Bose, B.K., Evaluation of membership functions for fuzzy logic controlled induction motor drive, IEEE 28th Annual Conference of the Industrial Electronics Society, 2002, vol. 1, pp. 229–234.Google Scholar
  13. 13.
    Barua, A., Mudunuri, L.S., and Kosheleva, O., Why trapezoidal and triangular membership functions work so well: Towards a theoretical explanation, J. Uncertain Syst., 2014, vol. 8, no. 3, pp. 164–168.Google Scholar
  14. 14.
    Mamdani, E.H. and Assilian, S., An experiment in linguistic synthesis with a fuzzy logic controller, Int. J. Man Mach. Stud., 1975, vol. 7, no. 1, pp. 1–13.CrossRefzbMATHGoogle Scholar
  15. 15.
    Jang, J.S.R., ANFIS: Adaptive-Network-Based Fuzzy Inference System, IEEE Trans. Syst. Man Cybern., 1993, vol. 23, no. 3, pp. 665–684.CrossRefGoogle Scholar
  16. 16.
    Sujatha, K.N. and Vaisakh, K., Implementation of adaptive neuro fuzzy inference system in speed control of induction motor drives, J. Intell. Learn. Syst. Appl., 2010, vol. 2, no. 2.Google Scholar
  17. 17.
    Jang, J.S.R., Sun, C.T., and Mizutani, E., Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, Prentice Hall Inc., 1997.Google Scholar
  18. 18.
    Jang, J.S.R. and Sun, C.T., Neuro-fuzzy modeling and control, Proc. IEEE, 1995, vol. 83, no. 3.Google Scholar
  19. 19.
    Maaref, H. and Barret, C., Sensor-based navigation of a mobile robot in an indoor environment, Rob. Auton. Syst., 2002, vol. 38, pp. 1–18.CrossRefzbMATHGoogle Scholar
  20. 20.
    Fraichard, T. and Garnier, P., Fuzzy control to drive car-like vehicles, Rob. Auton. Syst., 2001, vol. 34, pp. 1–22.CrossRefGoogle Scholar
  21. 21.
    Benet, G., Blanes, F., Simo, J.E., and Perez, P., Rob. Auton. Syst., 2002, vol. 10, pp. 255–266.CrossRefGoogle Scholar
  22. 22.
    Adarsh, S., Mohammed Kaleemmuddin, Dinesh Bose, and Ramachandran, K.I., Performance comparison of infrared and ultrasonic sensors for obstacles of different materials in vehicle/robot navigation applications, IOP Conf. Ser.: Mater. Sci. Eng., 2016, vol. 149, no. 1.Google Scholar
  23. 23.
    Vakula, D. and Yeshwanth Krishna Kolli, Low cost smart parking system for smart cities, Proceedings of 3rd Smart Manufacturing Summit, CII, New Delhi, 2017, pp. 66–70.Google Scholar
  24. 24.
    HC-SR04 data sheet. Accessed May 25, 2016.Google Scholar
  25. 25.
    GP2Y0A21YK0F-Sharp data sheet. gp2y0a21yk_e.pdf. Accessed May 28, 2016.Google Scholar

Copyright information

© Allerton Press, Inc. 2018

Authors and Affiliations

  1. 1.Department of Electronics and Communication Engineering, Othakkal Mandapam PostCoimbatoreIndia
  2. 2.Department of Mechanical Engineering Amrita School of EngineeringCoimbatore Amrita Vishwa VidyapeethamIndia

Personalised recommendations