Study of Collective Robotic Tasks Based on the Behavioral Model of the Agent

  • Fredy MartínezEmail author
  • Edwar Jacinto
  • Fernando Martínez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9375)


In recent years, much research has been devoted to the analysis, modeling and design of multi-agent robotic systems. Such systems are composed of a set of simple agents that self-organize to perform a task. Given the parallel structure, also happens to be a very robust solution. This paper focuses on the development of a behavioral model of each agent of the system, from which, defining simple behavior and interaction rules, it is possible to set the emergent behavior of the system. The interaction model takes elements observed in bacteria and establishes a structure at the agent level and the system level. The proposed model is validated through the design of a basic navigation task where the robots form autonomously groups without any external interaction or prior information of the environment or other robots.


Bio-inspired model Parallel system Path planing Robotics 



This work was supported by the District University Francisco Jos de Caldas, in part through CIDC, and partly by the Technological Faculty. The views expressed in this paper are not necessarily endorsed by District University. The authors thank the research groups DIGITI and ARMOS for the evaluation carried out on prototypes of ideas and strategies.


  1. 1.
    Camazine, S., Deneubourg, J., Franks, N., Sneyd, J., Theraulaz, G., Bonabeau, E.: Self-organization in Biological Systems. Princeton University Press, Princeton (2001). ISBN 978-0691012117 zbMATHGoogle Scholar
  2. 2.
    Chaohong, C., Rafal, G., Sanfelice, R.G., Teel, A.R.: Hybrid dynamical systems: Robust stability and control. Proc. Chinese Control Conf. CCC 2007, 29–36 (2007)Google Scholar
  3. 3.
    Gerkey, B., Vaughan, R.T., Howard, A.: The player/stage project: tools for multi-robot and distributed sensor systems. In: Proceedings IEEE 11th International Conference on Advanced Robotics ICAR 2003, pp. 317–323. IEEE, Coimbra (Portugal), June 2003Google Scholar
  4. 4.
    Gonzalez, A., Ghaffarkhah, A., Mostofi, Y.: An integrated framework for obstacle mapping with see-through capabilities using laser and wireless channel measurements. IEEE Sensors J. 14(1), 25–38 (2014)CrossRefGoogle Scholar
  5. 5.
    Hernandez-Martinez, E.G., Albino, J.M.F.: Hybrid architecture of multi-robot systems based on formation control and som neural networks. In: Proceedings of IEEE International Control Applications (CCA) Conference, pp. 941–946 (2011)Google Scholar
  6. 6.
    LaValle, S.M.: Planning Algorithms. Cambridge University Press, Cambridge (2006). CrossRefzbMATHGoogle Scholar
  7. 7.
    Martínez, F.H., Delgado, J.A.: Hardware emulation of bacterial quorum sensing. In: Huang, D.-S., Zhao, Z., Bevilacqua, V., Figueroa, J.C. (eds.) ICIC 2010. LNCS, vol. 6215, pp. 329–336. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  8. 8.
    Polani, D.: Measuring self-organization via observers. In: Banzhaf, W., Ziegler, J., Christaller, T., Dittrich, P., Kim, J.T. (eds.) ECAL 2003. LNCS (LNAI), vol. 2801, pp. 667–675. Springer, Heidelberg (2003) CrossRefGoogle Scholar
  9. 9.
    Prokopenko, M.: Advances in Applied Self-organizing Systems. Advanced Information and Knowledge Processing, 1st edn. Springer, Berlin (2008). ISBN 978-1-84628-981-1CrossRefGoogle Scholar
  10. 10.
    Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd edn. Pearson Prentice Hall, Englewood Cliffs (2002). ISBN 0137903952zbMATHGoogle Scholar
  11. 11.
    Santini, C., Tyrrell, A.: Investigating the properties of self-organization and synchronization in electronic systems. IEEE Trans. NanoBiosci. 8(3), 237–251 (2009). ISSN 1536–1241CrossRefGoogle Scholar
  12. 12.
    Vaughan, R.: Massively multi-robot simulation in stage. Swarm Intell. 2(2), 189–208 (2008)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Weikersdorfer, D., Conradt, J.: Event-based particle filtering for robot self-localization. In: 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 866–870 (2012)Google Scholar
  14. 14.
    Yoon, K.H., Lee, J.K., Kim, K.H., Park, B.S., Yoon, J.S.: Hybrid robust controller design for a two mass system with disturbance compensation. In: Proceedings of International Conference on Control, Automation and Systems, ICCAS 2008, pp. 1367–1372 (2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Fredy Martínez
    • 1
    Email author
  • Edwar Jacinto
    • 1
  • Fernando Martínez
    • 1
  1. 1.District University Francisco José de CaldasBogotáColombia

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