Advertisement

Artificial Life and Robotics

, Volume 23, Issue 4, pp 600–608 | Cite as

Evolutionary design method of probabilistic finite state machine for swarm robots aggregation

  • Yoshiaki KatadaEmail author
Original Article
  • 49 Downloads

Abstract

This paper proposes to use evolutionary computations to determine the parameters of probabilistic finite state machine controllers for swarm robots. The robots are evolved to perform an aggregation task. This problem was formulated as an optimization problem and solved by the PSO. Several computer simulations were conducted to investigate the validity of the proposed method. The results obtained in this paper show us that the proposed method is useful for the aggregation problem and the best evolved controllers are feasible as well as interpretable. This would be transferable to real swarm robots problems.

Keywords

Evolutionary swarm robotics Aggregation Probabilistic finite state machine 

References

  1. 1.
    Trianni V (2008) Evolutionary swarm robotics. Springer-Verlag, BerlinCrossRefGoogle Scholar
  2. 2.
    Brambilla M, Ferrante E, Birattari M, Dorigo M (2013) Swarm robotics: a review from the swarm engineering perspective. Swarm Intell 7(1):1–41CrossRefGoogle Scholar
  3. 3.
    Şahin E (2005) Swarm robotics: from sources of inspiration to domains of application. In: Şahin E, Spears WM (eds.) Swarm Robotics, Springer, New York, pp 10–20 (Lecture Notes in Computer Science 3342) CrossRefGoogle Scholar
  4. 4.
    Sutton RS, Barto AG (1998) Reinforcement learning: an introduction. MIT Press, CambridgeGoogle Scholar
  5. 5.
    Nolfi S, Floreano D (2000) Evolutionary robotics: the biology, intelligence, and technology of self-organizing machines. MIT Press, CambridgeGoogle Scholar
  6. 6.
    Brooks RA (1992) Artificial life and real robots. In: Varela FJ, Bourgine P (eds) Toward a Practice of Autonomous Systems, Proceedings of the First European Conference on Artificial Life, MIT Press, Cambridge, pp 3–10Google Scholar
  7. 7.
    Jakobi N (1997) Half-baked ad-hoc and noisy: minimal simulation for evolutionary robotics. In: Husbands P, Harvey I (eds) Proceedings of the fourth european conference on artificial life, MIT Press, Cambridge, pp 348–357Google Scholar
  8. 8.
    Miglino O, Lund HH, Nolfi S (1995) Evolving mobile robots in simulated and real environments. Artif Life 2:417–434CrossRefGoogle Scholar
  9. 9.
    Keymeulen D, Iwata M, Konaka K, Suzuki R, Kuniyoshi Y, Higuchi T (1998) Off-line model-free and on-line model-based evolution for tracking navigation using evolvable hardware. In: Husbands P, Meyer JA (eds) Evolutionary Robotics, First European Workshop EvoRobot 98, Springer, New York, pp 211–226 (Lecture Notes in Computer Science 1468) CrossRefGoogle Scholar
  10. 10.
    Katada Y, Ohkura K (2006) An update method of computer simulation for evolutionary robotics. In: Arai T, et al (eds) Intelligent autonomous systems 9 IOS Press, Amsterdam, pp 357–364Google Scholar
  11. 11.
    Garnier S, Jost C, Jeanson R, Gautrais J, Asadpour M, Caprari G, Theraulaz G (2005) Aggregation behaviour as a source of collective decision in a group of cockroach-like-robots. In: Capcarrere MS, Freitas AA, Bentley PJ, Johnson CG, Timmis J (eds) Advances in artificial life, Springer, New York, pp 169–178 (Lecture notes in Computer Science 3630) CrossRefGoogle Scholar
  12. 12.
    Soysal O, Bahçeci E, Şahin E (2007) Aggregation in swarm robotic systems: evolution and probabilistic control. Turk J Electr Eng Comput Sci 15(2):199–225Google Scholar
  13. 13.
    Gauci M, Chen J, Li W, Dodd TJ, Groß R (2014) Self-organised aggregation without computation. Int J Robot Res 33(9):1145–1161CrossRefGoogle Scholar
  14. 14.
    Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, vol 4, Perth, 27 November–1 December 1995, pp 1942–1948Google Scholar
  15. 15.
    Brooks R (1986) A robust layered control system for a mobile robot. IEEE J Robot Autom 2(1):14–23CrossRefGoogle Scholar
  16. 16.
    Pfeifer R, Scheier C (1999) Understanding intelligence. MIT Press, CambridgeGoogle Scholar
  17. 17.
    Graham RL, Sloane NJA (1990) Penny-packing and two-dimensional codes. Discrete Comput Geom 5(1):1–11MathSciNetCrossRefGoogle Scholar
  18. 18.
    Katada Y, Nishiguchi A, Moriwaki K, Watakabe R (2016) Swarm robotic network using Lévy flight in target detection problem. Artif Life Robot 21(3):295–301CrossRefGoogle Scholar
  19. 19.
    Open Dynamics Engine (ODE). http://ode.org/
  20. 20.
    Nouyan S, Campo A, Dorigo M (2008) Path formation in a robot swarm—self-organized strategies to find your way home. Swarm Intell 2(1):1–23CrossRefGoogle Scholar
  21. 21.
    Liu W, Winfield A, Sa J (2009) A macroscopic probabilistic model of adaptive foraging in swarm robotics systems. In: Proceedings of 6th vienna international conference on mathematical modelling, Special session on modelling the swarmGoogle Scholar
  22. 22.
    Labella TH, Dorigo M, Deneubourg JL (2006) Division of Labor in a group of robots inspired by ants’ foraging behavior. ACM Trans Auton Adapt Syst 1(1):4–25CrossRefGoogle Scholar

Copyright information

© ISAROB 2018

Authors and Affiliations

  1. 1.Setsunan UniversityNeyagawaJapan

Personalised recommendations