Swarm Intelligence

, Volume 2, Issue 2–4, pp 241–266 | Cite as

Modelling a wireless connected swarm of mobile robots

  • Alan F. T. Winfield
  • Wenguo Liu
  • Julien Nembrini
  • Alcherio Martinoli


It is a characteristic of swarm robotics that modelling the overall swarm behaviour in terms of the low-level behaviours of individual robots is very difficult. Yet if swarm robotics is to make the transition from the laboratory to real-world engineering realisation such models would be critical for both overall validation of algorithm correctness and detailed parameter optimisation. We seek models with predictive power: models that allow us to determine the effect of modifying parameters in individual robots on the overall swarm behaviour. This paper presents results from a study to apply the probabilistic modelling approach to a class of wireless connected swarms operating in unbounded environments. The paper proposes a probabilistic finite state machine (PFSM) that describes the network connectivity and overall macroscopic behaviour of the swarm, then develops a novel robot-centric approach to the estimation of the state transition probabilities within the PFSM. Using measured data from simulation the paper then carefully validates the PFSM model step by step, allowing us to assess the accuracy and hence the utility of the model.


Swarm robotics Modelling Wireless ad hoc network 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Supplementary material

Video file


  1. Agassounon, W., Martinoli, A., & Goodman, R. M. (2001). A scalable, distributed algorithm for allocating workers in embedded systems. In Proceedings of the IEEE conference on systems, man and cybernetics (pp. 3367–3373). Piscataway: IEEE Press. Google Scholar
  2. Agassounon, W., Martinoli, A., & Easton, K. (2004). Macroscopic modelling of aggregation experiments using embodied agents in teams of constant and time-varying sizes. Autonomous Robots, 17(2–3), 163–192. CrossRefGoogle Scholar
  3. Beni, G. (2005). From swarm intelligence to swarm robotics. In E. Şahin & W. M. Spears (Eds.), Lecture notes in computer science : Vol. 3342. Swarm robotics—SAB 2004 international workshop (pp. 1–9). Berlin: Springer. Google Scholar
  4. Berman, S., Halasz, A., Kumar, V., & Pratt, S. (2007). Algorithms for the analysis and synthesis of a bio-inspired swarm robotic system. In E. Şahin, W. M. Spears, & A. F. T. Winfield (Eds.), Lecture notes in computer science : Vol. 4433. Swarm robotics—second SAB 2006 international workshop (pp. 56–70). Berlin: Springer. Google Scholar
  5. Correll, N., & Martinoli, A. (2004). Modelling and optimisation of a swarm-intelligent inspection system. In R. Alami, H. Asama, & R. Chatila (Eds.), Proceedings of the 7th symposium on distributed autonomous robotic systems (DARS’04) (pp. 369–378). Berlin: Springer. Google Scholar
  6. Gerkey, B., Vaughan, R., & Howard, A. (2003). The player/stage project: tools for multi-robot and distributed sensor systems. In Proceedings of the 11th international conference on advanced robotics (pp. 317–323). Piscataway: IEEE Press. Google Scholar
  7. Ijspeert, A., Martinoli, A., Billard, A., & Gambardella, L. (2001). Collaboration through the exploitation of local interactions in autonomous collective robotics: the stick pulling experiment. Autonomous Robots, 11(2), 149–171. zbMATHCrossRefGoogle Scholar
  8. Kazadi, S., Chung, M., Lee, B., & Cho, R. (2004). On the dynamics of puck clustering systems. Robotics and Autonomous Systems, 46(1), 1–27. CrossRefGoogle Scholar
  9. Lerman, K., & Galstyan, A. (2002). Mathematical model of foraging in a group of robots: effect of interference. Autonomous Robots, 13(2), 127–141. zbMATHCrossRefGoogle Scholar
  10. Lerman, K., Galstyan, A., Martinoli, A., & Ijspeert, A. (2002). A macroscopic analytical model of collaboration in distributed robotic systems. Artificial Life, 7, 375–393. CrossRefGoogle Scholar
  11. Lerman, K., Martinoli, A., & Galstyan, A. (2005). A review of probabilistic macroscopic models for swarm robotic systems. In E. Şahin & W. M. Spears (Eds.), Lecture notes in computer science : Vol. 3342. Swarm robotics—SAB 2004 international workshop (pp. 143–152). Berlin: Springer. Google Scholar
  12. Martinoli, A., Ijspeert, A. J., & Mondada, F. (1999). Understanding collective aggregation mechanisms: from probabilistic modelling to experiments with real robots. Robotics and Autonomous Systems, 29(1), 51–63. CrossRefGoogle Scholar
  13. 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), 415–436. CrossRefGoogle Scholar
  14. Milutinovic, D., & Lima, P. (2006). Modeling and optimal centralized control of a large-size robotic population. IEEE Transactions on Robotics, 22(6), 1280–1285. CrossRefGoogle Scholar
  15. Nembrini, J. (2005). Minimalist coherent swarming of wireless networked autonomous mobile robots. PhD thesis, University of the West of England, Bristol, UK. Google Scholar
  16. Nembrini, J., Winfield, A. F. T., & Melhuish, C. (2002). Minimalist coherent swarming of wireless networked autonomous mobile robots. In From animals to animats (SAB’02) (pp. 373–382). Cambridge: MIT Press. Google Scholar
  17. Rouff, C., Truszkowski, W., Rash, J., & Hinchey, M. (2003). Formal approaches to intelligent swarms. In IEEE/NASA software engineering workshop (SEW’03) (pp. 51–57). Los Alamitos: IEEE Computer Society. Google Scholar
  18. Şahin, E. (2005). Swarm robotics: from sources of inspiration to domains of application. In E. Şahin & W. M. Spears (Eds.), Lecture notes in computer science : Vol. 3342. Swarm robotics—SAB 2004 international workshop (pp. 10–20). Berlin: Springer. Google Scholar
  19. Støy, K. (2001). Using situated communication in distributed autonomous mobile robotics. In Proceedings of the 7th Scandinavian conference on artificial intelligence (pp. 44–52). Amsterdam: IOS. Google Scholar
  20. Truszkowski, W., Hinchey, M., Rash, J., & Rouff, C. (2004). NASA’s swarm missions: the challenge of building autonomous software. IT Professional, 6(5), 47–52. CrossRefGoogle Scholar
  21. Winfield, A. F. T., & Holland, O. (2000). The application of wireless local area network technology to the control of mobile robots. Microprocessors and Microsystems, 23, 597–607. CrossRefGoogle Scholar
  22. Winfield, A. F. T., & Nembrini, J. (2006). Safety in numbers: fault-tolerance in robot swarms. International Journal of Modelling, Identification and Control, 1(1), 30–37. CrossRefGoogle Scholar
  23. Winfield, A. F. T., Harper, C., & Nembrini, J. (2006). Towards the application of swarm intelligence in safety-critical applications. In Proceedings of the 1st IET international conference on system safety (pp. 89–95). London: IET. CrossRefGoogle Scholar

Copyright information

© Springer Science + Business Media, LLC 2008

Authors and Affiliations

  • Alan F. T. Winfield
    • 1
  • Wenguo Liu
    • 1
  • Julien Nembrini
    • 2
  • Alcherio Martinoli
    • 2
  1. 1.Bristol Robotics LaboratoryUniversity of the West of EnglandBristolUK
  2. 2.Distributed Intelligent Systems and Algorithms LaboratoryÉcole Polytechnique Fédérale de LausanneLausanneSwitzerland

Personalised recommendations