Motion Controlling Using Finite-State Automata

  • Michal Jaluvka
  • Eva VolnaEmail author
  • Martin Kotyrba
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11432)


This paper, a discussion of fundamental finite-state algorithms, constitutes an approach from the perspective of dynamic system control. First, we describe fundamental properties of deterministic finite-state automata. We propose an algorithm focused on correct finite-state automaton state switching. This approach will then be used to propose a finite-state automaton to solve real-time movement in a maze. We also illustrate the use of such a proposed approach to control movement in a robotic system which requires correct states to achieve correct movement. Evaluation of achieved outputs is described in the conclusion, which also includes the future focus of the proposed way of finite-state automaton state switching.


Deterministic finite-state automaton Finite-state automaton state switching Movement in a maze Robotics motion controlling 



The research described here has been financially supported by University of Ostrava grant SGS04/PřF/2018. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not reflect the views of the sponsors.


  1. 1.
    Aqel, M.O., Issa, A., Khdair, M., ElHabbash, M., AbuBaker, M., Massoud, M.: Intelligent maze solving robot based on image processing and graph theory algorithms. In: International Conference on Promising Electronic Technologies (ICPET), pp. 48–53. IEEE (2017)Google Scholar
  2. 2.
    Berthe, V., Rigo, M.: Combinatorics, Words and Symbolic Dynamics. Cambridge University Press, Cambridge (2016)CrossRefGoogle Scholar
  3. 3.
    Broumi, S., Bakal, A., Talea, M., Smarandache, F., Vladareanu, L.: Applying Dijkstra algorithm for solving neutrosophic shortest path problem. In: International Conference on Advanced Mechatronic Systems (ICAMechS), pp. 412–416. IEEE (2016)Google Scholar
  4. 4.
    Čejková, J., Tóth, R., Braun, A., Branicki, M., Ueyama, D., Lagzi, I.: Shortest path finding in mazes by active and passive particles. In: Adamatzky, A. (ed.) Shortest Path Solvers. From Software to Wetware. ECC, vol. 32, pp. 401–408. Springer, Cham (2018). Scholar
  5. 5.
    Gordon, V.S., Matley, Z.: Evolving sparse direction maps for maze pathfinding. In: Congress on Evolutionary Computation, CEC 2004, vol. 1, pp. 835–838. IEEE (2004)Google Scholar
  6. 6.
    Kleene, S.C.: Representation of events in nerve nets and finite automate. In: Automata Studies, Annals of Mathematics Studies, pp. 2–42. Princeton University Press (1956)Google Scholar
  7. 7.
    Lafore, R.: Data Structures and Algorithms in Java. Sams Publishing (2017)Google Scholar
  8. 8.
    Martins, L.G.A., Cândido, R.P., Escarpinati, M.C., Vargas, P.A., de Oliveira, G.M.B.: An improved robot path planning model using cellular automata. In: Giuliani, M., Assaf, T., Giannaccini, M.E. (eds.) TAROS 2018. LNCS (LNAI), vol. 10965, pp. 183–194. Springer, Cham (2018). Scholar
  9. 9.
    Roche, E., Schabes, Y.: Finite-State Language Processing. MIT Press, Cambridge (1997)Google Scholar
  10. 10.
    Salomaa, A.: Theory of Automata. Elsevier (2014)Google Scholar
  11. 11.
    Straubing, H.: Finite Automata, Formal Logic, and Circuit Complexity. Springer, New York (2012). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Informatics and ComputersUniversity of OstravaOstravaCzech Republic

Personalised recommendations