Advertisement

Analysing Robot Swarm Decision-Making with Bio-PEPA

  • Mieke Massink
  • Manuele Brambilla
  • Diego Latella
  • Marco Dorigo
  • Mauro Birattari
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7461)

Abstract

We present a novel method to analyse swarm robotics systems based on Bio-PEPA. Bio-PEPA is a process algebraic language originally developed to analyse biochemical systems. Its main advantage is that it allows different kinds of analyses of a swarm robotics system starting from a single description. In general, to carry out different kinds of analysis, it is necessary to develop multiple models, raising issues of mutual consistency. With Bio-PEPA, instead, it is possible to perform stochastic simulation, fluid flow analysis and statistical model checking based on the same system specification. This reduces the complexity of the analysis and ensures consistency between analysis results. Bio-PEPA is well suited for swarm robotics systems, because it lends itself well to modelling distributed scalable systems and their space-time characteristics. We demonstrate the validity of Bio-PEPA by modelling collective decision-making in a swarm robotics system and we evaluate the result of different analyses.

Keywords

Short Path Model Check Stochastic Simulation Active Team Goal Area 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aziz, A., Sanwal, K., Singhal, V., Brayton, R.: Model checking Continuous Time Markov Chains. ACM Transactions on Computational Logic 1(1), 162–170 (2000)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Baier, C., Katoen, J.-P., Hermanns, H.: Approximate Symbolic Model Checking of Continuous-Time Markov Chains (Extended Abstract). In: Baeten, J.C.M., Mauw, S. (eds.) CONCUR 1999. LNCS, vol. 1664, pp. 146–162. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  3. 3.
    Brambilla, M., Pinciroli, C., Birattari, M., Dorigo, M.: Property-driven design for swarm robotics. In: Proceedings of 11th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2012. IFAAMAS ( in press, 2012)Google Scholar
  4. 4.
    Ciocchetta, F., Duguid, A., Gilmore, S., Guerriero, M.L., Hillston, J.: The Bio-PEPA Tool Suite. In: Proc. of the 6th Int. Conf. on Quantitative Evaluation of Systems (QEST 2009), pp. 309–310. IEEE Computer Society, Washington, DC (2009)CrossRefGoogle Scholar
  5. 5.
    Ciocchetta, F., Hillston, J.: Bio-PEPA: An extension of the process algebra PEPA for biochemical networks. ENTCS 194(3), 103–117 (2008)Google Scholar
  6. 6.
    Ciocchetta, F., Hillston, J.: Bio-PEPA: A framework for the modelling and analysis of biological systems. TCS 410(33-34), 3065–3084 (2009)MathSciNetzbMATHCrossRefGoogle Scholar
  7. 7.
    Şahin, E.: Swarm Robotics: From Sources of Inspiration to Domains of Application. In: Şahin, E., Spears, W.M. (eds.) Swarm Robotics 2004. LNCS, vol. 3342, pp. 10–20. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  8. 8.
    Dixon, C., Winfield, A., Fisher, M.: Towards Temporal Verification of Emergent Behaviours in Swarm Robotic Systems. In: Groß, R., Alboul, L., Melhuish, C., Witkowski, M., Prescott, T.J., Penders, J. (eds.) TAROS 2011. LNCS, vol. 6856, pp. 336–347. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    Gillespie, D.T.: Exact stochastic simulation of coupled chemical reactions. The Journal of Physical Chemistry 81(25), 2340–2361 (1977)CrossRefGoogle Scholar
  10. 10.
    Hillston, J.: Fluid flow approximation of PEPA models. In: Proceedings of the 2th International Conference on Quantitative Evaluation of SysTems (QEST 2005), pp. 33–43. IEEE Computer Society, Washington, DC (2005)CrossRefGoogle Scholar
  11. 11.
    Ijspeert, A., Martinoli, A., Billard, A., Gambardella, L.M.: Collaboration through the exploitation of local interactions in autonomous collective robotics: The stick pulling experiment. Autonomous Robots 11, 149–171 (2001)zbMATHCrossRefGoogle Scholar
  12. 12.
    Kleinrock, L.: Queueing Systems. Theory, vol. 1. Wiley, New York (1975)zbMATHGoogle Scholar
  13. 13.
    Konur, S., Dixon, C., Fisher, M.: Analysing robot swarm behaviour via probabilistic model checking. Robotics and Autonomous Systems 60(2), 199–213 (2012)CrossRefGoogle Scholar
  14. 14.
    Kurtz, T.: Solutions of ordinary differential equations as limits of pure jump Markov processes. Journal of Applied Probability 7, 49–58 (1970)MathSciNetzbMATHCrossRefGoogle Scholar
  15. 15.
    Kwiatkowska, M., Norman, G., Parker, D.: PRISM 4.0: Verification of Probabilistic Real-Time Systems. In: Gopalakrishnan, G., Qadeer, S. (eds.) CAV 2011. LNCS, vol. 6806, pp. 585–591. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  16. 16.
    Lerman, K., Martinoli, A., Galstyan, A.: A Review of Probabilistic Macroscopic Models for Swarm Robotic Systems. In: Şahin, E., Spears, W.M. (eds.) Swarm Robotics 2004. LNCS, vol. 3342, pp. 143–152. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  17. 17.
    Martinoli, A., Easton, K., Agassounon, W.: Modeling swarm robotic systems: a case study in collaborative distributed manipulation. The International Journal of Robotics Research 23(4-5), 415–436 (2004)CrossRefGoogle Scholar
  18. 18.
    Massink, M., Latella, D., Bracciali, A., Harrison, M., Hillston, J.: Scalable context-dependent analysis of emergency egress models. Formal Aspects of Computing 24(2), 267–302 (2012)CrossRefGoogle Scholar
  19. 19.
    Massink, M., Latella, D., Bracciali, A., Hillston, J.: Modelling Non-linear Crowd Dynamics in Bio-PEPA. In: Giannakopoulou, D., Orejas, F. (eds.) FASE 2011. LNCS, vol. 6603, pp. 96–110. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  20. 20.
    Massink, M., Brambilla, M., Latella, D., Dorigo, M., Birattari, M.: Analysing robot swarm decision-making with Bio-PEPA: Complete data (2012), Supplementary information page at http://iridia.ulb.ac.be/supp/IridiaSupp2012-012/
  21. 21.
    Montes de Oca, M.A., Ferrante, E., Scheidler, A., Pinciroli, C., Birattari, M., Dorigo, M.: Majority-rule opinion dynamics with differential latency: A mechanism for self-organized collective decision-making. Swarm Intelligence 5(3-4), 305–327 (2011)CrossRefGoogle Scholar
  22. 22.
    Scheidler, A.: Dynamics of majority rule with differential latencies. Phys. Rev. E 83, 031116 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Mieke Massink
    • 1
  • Manuele Brambilla
    • 2
  • Diego Latella
    • 1
  • Marco Dorigo
    • 2
  • Mauro Birattari
    • 2
  1. 1.Istituto di Scienza e Tecnologie dell’Informazione ‘A. Faedo’ (ISTI)CNR PisaItaly
  2. 2.IRIDIAUniversité Libre de BruxellesBrusselsBelgium

Personalised recommendations