Skip to main content

Soft Computing-Based Control System of Intelligent Robot Navigation

  • Conference paper
  • First Online:
Intelligent Information and Database Systems (ACIIDS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12034))

Included in the following conference series:

  • 1653 Accesses

Abstract

This paper focuses on the study of intelligent navigation techniques which are capable of navigating a mobile robot autonomously in unknown environments in real-time. We primarily focused on a soft computing-based control system of autonomous robot behaviour. The soft computing methods included artificial neural networks and fuzzy logic. Using them, it was possible to control autonomous robot behaviour. Based on defined behaviour, this device was able to deduce a corresponding reaction to an unknown situation. Real robotic equipment was represented by a Lego Mindstorms EV3 robot. The outcomes of all experiments were analysed in the conclusion.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abadi, D.N.M., Khooban, M.H.: Design of optimal Mamdani-type fuzzy controller for nonholonomic wheeled mobile robots. J. King Saud Univ. Eng. Sci. 27(1), 92–100 (2015)

    Article  Google Scholar 

  2. Barton, A., Volna, E., Kotyrba, M.: Big data filtering through adaptive resonance theory. In: Nguyen, N.T., Tojo, S., Nguyen, L.M., Trawiński, B. (eds.) ACIIDS 2017. LNCS (LNAI), vol. 10192, pp. 382–391. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-54430-4_37

    Chapter  Google Scholar 

  3. Black, L.: A Worm’s mind in a Lego body. I Programmer, May 2019. http://www.i-programmer.info/news/105-artificial-intelligence/7985-a-worms-mind-in-a-lego-body.html

  4. De Silva, W.: Intelligent Control: Fuzzy Logic Applications. CRC Press, Boca Raton (2018)

    Google Scholar 

  5. Farooq, U., Amar, M., Asad, M.U., Hanif, A., Saleh, S.O.: Design and implementation of neural network based controller for mobile robot navigation in unknown environments. Int. J. Comput. Electrical Eng. 6(2), 83–89 (2014)

    Article  Google Scholar 

  6. Kim, P.K., Jung, S.: Experimental studies of neural network control for one-wheel mobile robot. J. Control Sci. Eng. 2012, 12 (2012). Article ID 194397

    Article  MathSciNet  Google Scholar 

  7. Konvicka, J., Kotyrba, M., Volna, E., Habiballa, H., Bradac, V.: Adaptive control of EV3 robot using mobile devices and fuzzy logic. In: Kim, K.J., Baek, N. (eds.) ICISA 2018. LNEE, vol. 514, pp. 389–399. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-1056-0_40

    Chapter  Google Scholar 

  8. Matarić, M.J., Arkin, R.C.: The Robotics Primer. MIT Press, London (2007)

    Google Scholar 

  9. Markoski, A., Vukosavljev, S., Kukolj, D., Pletl, S.: Mobile robot control using self-learning neural network. In: 7th International Symposium on Intelligent Systems and Informatics, pp. 45–48. IEEE (2009)

    Google Scholar 

  10. Mo, H., Tang, Q., Meng, L.: Behavior-based fuzzy control for mobile robot navigation. Mathematical Problems in Engineering 2013, 10 (2013). Article ID 561451

    MATH  Google Scholar 

  11. Motlagh, O.R.E., Hong, T.S., Ismail, N.: Development of a new minimum avoidance system for a behavior-based mobile robot. Fuzzy Sets Syst. 160(13), 1929–1946 (2009)

    Article  MathSciNet  Google Scholar 

  12. Peri, V.M., Simon, D.: Fuzzy logic control for an autonomous robot. In: Annual Meeting of the North American Fuzzy Information Processing Society, pp. 337–342. IEEE (2005)

    Google Scholar 

  13. Rojas, R.: Neural Networks: A Systematic Introduction. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-61068-4

    Book  MATH  Google Scholar 

  14. Tripathi, G.N., Rihani, V.: Motion planning of an autonomous mobile robot using artificial neural network. arXiv preprint arXiv:1207.4931 (2012)

  15. Vinogradov, A., Terentev, A., Kochetkov, M., Petrov, V.: Model of fuzzy regulator of mobile robot motion control system. In: 2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, pp. 2109–2112. IEEE (2019)

    Google Scholar 

  16. Volna, E., Kotyrba, M., Jaluvka, M.: Intelligent robot’s behavior based on fuzzy control system. In: International Conference on Industrial Engineering, Management Science and Application, pp. 34 – 38. IEEE (2016)

    Google Scholar 

  17. Volna, E., Kotyrba, M., Zacek, M., Bartoň, A.: Emergence of an autonomous robot’s behaviour. In: European Conference on Modelling and Simulation, Bulgaria 2015, pp. 462–468 (2015)

    Google Scholar 

  18. Wang, D., Zhang, Y., Si, W.: Behavior-based hierarchical fuzzy control for mobile robot navigation in dynamic environment. In: Proceedings of the Chinese Control and Decision Conference, pp. 2419–2424 (2011)

    Google Scholar 

Download references

Acknowledgments

The research described here has been financially supported by the University of Ostrava grant SGS05/PRF/2019.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Eva Volná .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Volná, E., Kotyrba, M., Bradac, V. (2020). Soft Computing-Based Control System of Intelligent Robot Navigation. In: Nguyen, N., Jearanaitanakij, K., Selamat, A., Trawiński, B., Chittayasothorn, S. (eds) Intelligent Information and Database Systems. ACIIDS 2020. Lecture Notes in Computer Science(), vol 12034. Springer, Cham. https://doi.org/10.1007/978-3-030-42058-1_32

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-42058-1_32

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-42057-4

  • Online ISBN: 978-3-030-42058-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics