Swarm Intelligence

, Volume 2, Issue 2–4, pp 97–120 | Cite as

Self-organized flocking in mobile robot swarms

  • Ali E. Turgut
  • Hande Çelikkanat
  • Fatih Gökçe
  • Erol Şahin
Article

Abstract

In this paper, we study self-organized flocking in a swarm of mobile robots. We present Kobot, a mobile robot platform developed specifically for swarm robotic studies. We describe its infrared-based short range sensing system, capable of measuring the distance from obstacles and detecting kin robots, and a novel sensing system called the virtual heading system (VHS) which uses a digital compass and a wireless communication module for sensing the relative headings of neighboring robots.

We propose a behavior based on heading alignment and proximal control that is capable of generating self-organized flocking in a swarm of Kobots. By self-organized flocking we mean that a swarm of mobile robots, initially connected via proximal sensing, is able to wander in an environment by moving as a coherent group in open space and to avoid obstacles as if it were a “super-organism”. We propose a number of metrics to evaluate the quality of flocking. We use a default set of behavioral parameter values that can generate acceptable flocking in robots, and analyze the sensitivity of the flocking behavior against changes in each of the parameters using the metrics that were proposed. We show that the proposed behavior can generate flocking in a small group of physical robots in a closed arena as well as in a swarm of 1000 simulated robots in open space. We vary the three main characteristics of the VHS, namely: (1) the amount and nature of noise in the measurement of heading, (2) the number of VHS neighbors, and (3) the range of wireless communication. Our experiments show that the range of communication is the main factor that determines the maximum number of robots that can flock together and that the behavior is highly robust against the other two VHS characteristics. We conclude by discussing this result in the light of related theoretical studies in statistical physics.

Keywords

Swarm robotics Self-organization Flocking 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aldana, M., & Huepe, C. (2003). Phase transitions in self-driven many-particle systems and related non-equilibrium models: A network approach. Journal of Statistical Physics, 112(1/2), 135–153. MATHCrossRefGoogle Scholar
  2. Balch, T. (2000). Hierarchic social entropy: An information theoretic measure of robot group diversity. Autonomous Robots, 8(3), 209–237. CrossRefGoogle Scholar
  3. Baldassarre, G. (2008). Self-organization as phase transition in decentralized groups of robots: A study based on Boltzmann entropy. In P. Mikhail (Ed.), Advances in applied self-organizing systems (pp. 127–146). Berlin: Springer. CrossRefGoogle Scholar
  4. Ballerini, M., Cabibbo, N., Candelier, R., Cavagna, A., Cisbani, E., Giardina, I., Lecomte, V., Orlandi, A., Parisi, G., Procaccini, A., Viale, M., & Zdravkovic, V. (2008). Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study. Proceedings of the National Academy of Sciences, 105(4), 1232–1237. CrossRefGoogle Scholar
  5. Beason, R. C. (2005). Mechanisms of magnetic orientation in birds. Integrative and Comparative Biology, 45(3), 565–573. CrossRefGoogle Scholar
  6. Camazine, S., Deneubourg, J.-L., Franks, N. R., Sneyd, J., Theraulaz, G., & Bonabeau, E. (2001). Self-Organization in Biological Systems. New Jersey: Princeton University Press. Google Scholar
  7. Campo, A., Nouyan, S., Birattari, M., Groß, R., & Dorigo, M. (2006). Negotiation of goal direction for cooperative transport. In M. Dorigo et al. (Eds.), Lecture notes in computer science: Vol. 4150. Ant colony optimization and swarm intelligence: 5th international workshop, ANTS 2006 (pp. 191–202). Berlin: Springer. CrossRefGoogle Scholar
  8. Çelikkanat, H., Turgut, A. E., Gökçe, F., & Şahin, E. (2007). Evaluation of robustness in self-organized flocking (Tech. Rep. METU-CENG-TR-2008-02). Dept. of Computer Eng., Middle East Tech. Univ., Ankara, Turkey. Google Scholar
  9. Correll, N., Sempo, G., de Meneses, Y. L., Halloy, J., Deneubourg, J.-L., & Martinoli, A. (2006). SwisTrack: A tracking tool for multi-unit robotic and biological systems. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (pp. 2185–2191). New Jersey: IEEE Press. CrossRefGoogle Scholar
  10. Couzin, I. (2007). Collective minds. Nature, 445, 715. CrossRefGoogle Scholar
  11. Dalgaard, P. (2004). Introductory statistics with R, 3rd edn. Statistics and computing. New York: Springer. Google Scholar
  12. Gregoire, G., Chate, H., & Tu, Y. (2003). Moving and staying together without a leader. Physica D, 181, 157–170. MATHCrossRefMathSciNetGoogle Scholar
  13. Hayes, A., & Dormiani-Tabatabaei, P. (2002). Self-organized flocking with agent failure: Off-line optimization and demonstration with real robots. In Proceedings of the IEEE international conference on robotics and automation (pp. 3900–3905). New Jersey: IEEE Press. Google Scholar
  14. Kelly, I., & Keating, D. (1996). Flocking by the fusion of sonar and active infrared sensors on physical autonomous robots. In Proceedings of the third international conference on mechatronics and machine vision in practice (Vol. 1, pp. 14–17). Guimarães: Universidade do Minho. Google Scholar
  15. Kruszelnicki, K. S. (2008). Physics of flocks. http://www.abc.net.au/science/k2/moments/gmis9845.htm.
  16. Matarić, M. J. (1994). Interaction and intelligent behavior. Ph.D. thesis, MIT. Google Scholar
  17. Mermin, N. D., & Wagner, H. (1966). Absence of ferromagnetism or antiferromagnetism in one or two-dimensional isotropic Heisenberg models. Physical Review Letters, 17(22), 1133–1136. CrossRefGoogle Scholar
  18. Miglino, O., Lund, H. H., & Nolfi, S. (1995). Evolving mobile robots in simulated and real environments. Artificial Life, 2(4), 417–434. CrossRefGoogle Scholar
  19. Moshtagh, N., Jadbabaie, A., & Daniilidis, K. (2006). Vision-based control laws for distributed flocking of nonholonomic agents. In Proceedings of the IEEE international conference on robotics and automation (pp. 2769–2774). New Jersey: IEEE Press. Google Scholar
  20. Nembrini, J. (2005). Minimalist coherent swarming of wireless networked autonomous mobile robots. Google Scholar
  21. Nembrini, J., Winfield, A. F. T., & Melhuish, C. (2002). Minimalist coherent swarming of wireless networked autonomous mobile robots. In B. Hallam, D. Floreno, J. Hallam, G. Hayes, & J.-A. Meyer (Eds.), Proceedings of the 7th international conference on the simulation of adaptive behavior conference (Vol. 7, pp. 273–382). Cambridge: MIT Press. Google Scholar
  22. Okubo, A. (1986). Dynamical aspects of animal grouping: Swarms, schools, flocks, and herds. Advances in Biophysics, 22, 1–94. CrossRefGoogle Scholar
  23. Parrish, J. K., Viscido, S. V., & Grünbaum, D. (2002). Self-organized fish schools: An examination of emergent properties. The Biological Bulletin, 202, 296–305. CrossRefGoogle Scholar
  24. Partridge, B. (1982). The structure and function of fish schools. Scientific American, 246, 114–123. CrossRefGoogle Scholar
  25. Pitcher, T. J., & Parrish, J. K. (1993). Functions of shoaling behavior in teleosts. In T. J. Pitcher (Ed.), Behaviour of teleost fishes (pp. 363–439). London: Chapman and Hall. Google Scholar
  26. Regmi, A., Sandoval, R., Byrne, R., Tanner, H., & Abdallah, C. (2005). Experimental implementation of flocking algorithms in wheeled mobile robots. In Proceedings of American control conference (Vol. 7, pp. 4917–4922). New Jersey: IEEE Press. CrossRefGoogle Scholar
  27. Reynolds, C. (1987). Flocks, herds and schools: A distributed behavioral model. In Proceedings of the 14th annual conference on computer graphics and interactive techniques (SIGGRAPH ’87) (pp. 25–34). New York: ACM Press. CrossRefGoogle Scholar
  28. Simon, G., Volgyesi, P., Maroti, M., & Ledeczi, A. (2003). Simulation-based optimization of communication protocols for large-scale wireless sensor networks. In IEEE aerospace conference (Vol. 3, pp. 1339–1346). New Jersey: IEEE Press. Google Scholar
  29. Simons, A. M. (2004). Many wrongs: the advantage of group navigation. Trends in Ecology & Evolution, 19(9), 453–455. CrossRefGoogle Scholar
  30. Simpson, S. J., Sword, G. A., Lorch, P. D., & Couzin, I. D. (2006). Cannibal crickets on a forced march for protein and salt. Proceedings of the National Academy of Sciences, 103(11), 4152–4156. CrossRefGoogle Scholar
  31. Toner, J., & Tu, Y. (1998). Flocks, herds, and schools: A quantitative theory of flocking. Physical Review E, 58, 4828–4858. CrossRefMathSciNetGoogle Scholar
  32. Turgut, A. E., Gökçe, F., Çelikkanat, H., Bayındır, L., & Şahin, E. (2007). Kobot: A mobile robot designed specifically for swarm robotics research (Tech. Rep. METU-CENG-TR-2007-05). Dept. of Computer Eng., Middle East Tech. Univ., Ankara, Turkey. Google Scholar
  33. Turgut, A. E., Çelikkanat, H., Gökçe, F., & Şahin, E. (2008). Self-organized flocking with a mobile robot swarm. In: Proceedings of the 7th international conference on autonomous agents and multiagent systems, AAMAS 2008 (pp. 39–46). International Foundation for Autonomous Agents and Multiagent Systems. Google Scholar
  34. Vicsek, T., Czirók, A., Ben-Jacob, E., Cohen, I., & Shochet, O. (1995). Novel type of phase transition in a system of self-driven particles. Physical Review Letters, 75(6), 1226–1229. CrossRefGoogle Scholar
  35. Wallraff, H. G. (2005). Avian navigation: Pigeon homing as a paradigm. Berlin: Springer. Google Scholar
  36. Wiltschko, W., & Wiltschko, R. (2005). Magnetic orientation and magnetoreception in birds and other animals. Journal of Comparative Physiology A: Neuroethology, Sensory, Neural, and Behavioral Physiology, 191(8), 675–693. CrossRefGoogle Scholar

Copyright information

© Springer Science + Business Media, LLC 2008

Authors and Affiliations

  • Ali E. Turgut
    • 1
  • Hande Çelikkanat
    • 1
  • Fatih Gökçe
    • 1
  • Erol Şahin
    • 1
  1. 1.Kovan Research Lab., Dept. of Computer Eng.Middle East Technical UniversityAnkaraTurkey

Personalised recommendations