On the use of Bio-PEPA for modelling and analysing collective behaviours in swarm robotics
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In this paper we analyse a swarm robotics system using Bio-PEPA. Bio-PEPA is a process algebra language originally developed to analyse biochemical systems. A swarm robotics system can be analysed at two levels: the macroscopic level, to study the collective behaviour of the system, and the microscopic level, to study the robot-to-robot and robot-to-environment interactions. In general, multiple models are necessary to analyse a system at different levels. However, developing multiple models increases the effort needed to analyse a system and raises issues about the consistency of the results. Bio-PEPA, instead, allows the researcher to perform stochastic simulation, fluid flow (ODE) analysis and statistical model checking using a single description, reducing the effort necessary to perform the analysis and ensuring consistency between the results. Bio-PEPA is well suited for swarm robotics systems: by using Bio-PEPA it is possible to model distributed systems and their space-time characteristics in a natural way. We validate our approach by modelling a collective decision-making behaviour.
KeywordsSwarm robotics Modelling Bio-PEPA Fluid flow analysis Statistical model checking
The research leading to the results presented in this paper has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007–2013)/ERC grant agreement no. 246939, and by the EU project ASCENS, 257414. Manuele Brambilla, Mauro Birattari and Marco Dorigo acknowledge support from the F.R.S.-FNRS of Belgium’s Wallonia-Brussels Federation. Diego Latella has been partially supported by Project TRACE-IT—PAR FAS 2007–2013—Regione Toscana. The authors would like to thank Stephen Gilmore and Allan Clark (Edinburgh University) for their help with the Bio-PEPA tool suite and templates.
- Baier, C., Katoen, J.-P., & Hermanns, H. (1999). Approximate symbolic model checking of continuous-time Markov chains. In Lecture notes in computer science: Vol. 1664. Concur ’99 (pp. 146–162). Heidelberg: Springer. Google Scholar
- Benkirane, S., Norman, R., Scott, E., & Shankland, C. (2012). Measles epidemics and PEPA: an exploration of historic disease dynamics using process algebra. In D. Giannakopoulou & D. Méry (Eds.), Lecture notes in computer science: Vol. 7436. FM 2012: formal methods (pp. 101–115). Berlin: Springer. CrossRefGoogle Scholar
- Bornstein, B., Doyle, J., Finney, A., Funahashi, A., Hucka, M., Keating, S., Kovitz, H. K. B., Matthews, J., Shapiro, B., & Schilstra, M. (2004). Evolving a lingua franca and associated software infrastructure for computational systems biology: the systems biology markup language (SBML) project. Systems Biology, 1, 4153. Google Scholar
- Bortolussi, L., & Hillston, J. (2012). Fluid model checking. In M. Koutny & I. Ulidowski (Eds.), Lecture notes in computer science: Vol. 7454. CONCUR (pp. 333–347). Berlin: Springer. Google Scholar
- Brambilla, M., Pinciroli, C., Birattari, M., & Dorigo, M. (2012). Property-driven design for swarm robotics. In Proceedings of 11th international conference on autonomous agents and multiagent systems (AAMAS 2012) (pp. 139–146). IFAAMAS. Google Scholar
- Burch, J., Clarke, E., McMillan, K., & Dill, D. (1990). Sequential circuit verification using symbolic model checking. In Proceedings of the 27th design automation conference (pp. 46–51). Washington: IEEE Press. Google Scholar
- Ciocchetta, F., & Hillston, J. (2012). Bio-PEPA http://www.biopepa.org. Last checked on October 2012.
- Eaton, J. W. (2002). GNU octave manual. London: Network Theory Ltd. Google Scholar
- Evans, W., Mermoud, G., & Martinoli, A. (2010). Comparing and modeling distributed control strategies for miniature self-assembling robots. In IEEE international conference on robotics and automation (ICRA) (pp. 1438–1445). Google Scholar
- Gilat, A. (2004). MATLAB: an introduction with applications (2nd ed.). New York: Wiley. Google Scholar
- Holzmann, G. J. (1991). Design and validation of computer protocols. Upper Saddle River: Prentice-Hall Google Scholar
- Kwiatkowska, M., Norman, G., & Parker, D. (2011). PRISM 4.0: verification of probabilistic real-time systems. In Lecture notes in computer science: Vol. 6806. Proceedings of 23rd international conference on computer aided verification (CAV’11) (pp. 585–591). Heidelberg: Springer. CrossRefGoogle Scholar
- Martinoli, A., Easton, K., & Agassounon, W. (2004). Modeling swarm robotic systems: a case study in collaborative distributed manipulation. International Journal of Robotics Research, 23(4–5), 415–436. Google Scholar
- Massink, M., & Latella, D. (2012). Fluid analysis of foraging ants. In M. Sirjani (Ed.), Lecture notes in computer science: Vol. 7274. Coordination (pp. 152–165). Heidelberg: Springer. Google Scholar
- Massink, M., Latella, D., Bracciali, A., & Hillston, J. (2011a). Modelling non-linear crowd dynamics in Bio-PEPA. In D. Giannakopoulou & F. Orejas (Eds.), Lecture notes in computer science: Vol. 6603. FASE (pp. 96–110). Heidelberg: Springer. Google Scholar
- Massink, M., Brambilla, M., Latella, D., Dorigo, M., & Birattari, M. (2012a). Analysing robot swarm decision-making with Bio-PEPA: complete data. Supplementary information page at http://iridia.ulb.ac.be/supp/IridiaSupp2012-012/.
- Mather, T., & Hsieh, M. (2012). Ensemble synthesis of distributed control and communication strategies. In IEEE international conference on robotics and automation (ICRA) (pp. 4248–4253). Google Scholar
- Montes de Oca, M. A., Ferrante, E., Scheidler, A., Pinciroli, C., Birattari, M., & Dorigo, M. (2011). Majority-rule opinion dynamics with differential latency: a mechanism for self-organized collective decision-making. Swarm Intelligence, 5(3–4), 305–327. Google Scholar
- Nimal, V. (2010). Statistical approaches for probabilistic model checking. MSc mini-project dissertation, Oxford University Computing Laboratory Google Scholar
- Tschaikowski, M., & Tribastone, M. (2012). Exact fluid lumpability for Markovian process algebra. In M. Koutny & I. Ulidowski (Eds.), Lecture notes in computer science: Vol. 7454. CONCUR 2012—concurrency theory: 23rd international conference (pp. 380–394). Heidelberg: Springer. Google Scholar
- Valentini, G., Birattari, M., & Dorigo, M. (2013). Majority rule with differential latency: an absorbing Markov chain to model consensus. In European conference on complex systems (ECCS’12). Google Scholar
- Zarzhitsky, D., Spears, D., Thayer, D., & Spears, W. (2005). Agent-based chemical plume tracing using fluid dynamics. In M. Hinchey, J. Rash, W. Truszkowski, & C. Rouff (Eds.), Lecture notes in computer science: Vol. 3228. Formal approaches to agent-based systems (pp. 146–160). Heidelberg: Springer. CrossRefGoogle Scholar