Skip to main content
Log in

Leader-based versus soft control of multi-agent swarms

  • Special Feature: Original Article
  • Published:
Artificial Life and Robotics Aims and scope Submit manuscript

Abstract

We focus on the control of heterogeneous swarms of agents that evolve in a random environment. Control is achieved by introducing special agents: leader and infiltrated (shill) agents. A refined distinction is made between hidden and apparent controlling agents. For each case, we provide an analytically solvable example of swarm dynamics.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Sartoretti G, Hongler MO, Elias de Oliveira M, Mondada F (2014) Decentralized self-selection of swarm trajectories: from dynamical system theory to robotic implementation. Swarm Intell 8(4):329–351

    Article  Google Scholar 

  2. Michini M, Rastgoftar H, Hsieh MA, Jayasuriya S (2014) Distributed formation control for collaborative tracking of manifolds in flows. In: Proceedings of the American Control Conference, pp 3874–3880

  3. Collignon B, Deneubourg JL, Detrain C (2012) Leader-based and self-organized communication: Modelling group-mass recruitment in ants. J Theor Biol 313:7986

    Article  MathSciNet  MATH  Google Scholar 

  4. Han Jing, Li Ming, Guo Lei (2006) Soft control on collective behavior of a group of autonomous agents by a shill agent. J Syst Sci Complex 19(1):5462

    Article  MathSciNet  Google Scholar 

  5. Deneubourg JLH, Bleuler Gribovskiy A, Halloy J, Mondada F (2010) Towards mixed societies of chickens and robots. In: Proceedings of the International Conference on Intelligent Robots and Systems, pp 4722–4728

  6. Krause J, Winfield AFT, Deneubourg J (2011) Interactive robots in experimental biology. Trends Ecol Evol 26(7):369375

    Article  Google Scholar 

  7. Braitenberg V (1984) Vehicles: experiments in synthetic psychology. MIT Press, Cambridge

    Google Scholar 

  8. Krishnanand KN, Ghose D (2005) Formations of minimalist mobile robots using local-templates and spatially distributed interactions. Robot Autonom Syst 53(3–4):194213

    Google Scholar 

  9. Yang T, Mehta PG, Meyn SP (2013) Feedback particle filter. IEEE Trans Autom Control 58(10):2465–2480

    Article  MathSciNet  Google Scholar 

  10. Sartoretti G, Hongler MO, Filliger R (2014) The estimation problem and heterogeneous swarms of autonomous agents. In: Proceedings of the Stochastic Modeling Techniques and Data Analysis International Conference

  11. Sartoretti G, Hongler M-O (2013) Soft control of swarms: analytical approach. Proc Int Conf Agents Artif Intell 1:147–153

    Google Scholar 

  12. Banner AI, Karatzas Ichiba T, Papathanakos V, Fernhold R (2011) Hybrid atlas model. Ann Appl Prob 21(2):609–644

  13. Filliger R, Hongler MO, Blanchard P (2006) Soluble models for dynamics driven by a super-diffusive noise. Phys A 370:301–355

    Article  Google Scholar 

Download references

Acknowledgments

We thank M.-O. Hongler for numerous discussions in the writing of this paper. The author is funded by the Swiss National Science Foundation.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Guillaume Sartoretti.

Additional information

This work was presented in part at the 1st International Symposium on Swarm Behavior and Bio-Inspired Robotics, Kyoto, Japan, October 28–30, 2015.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sartoretti, G. Leader-based versus soft control of multi-agent swarms. Artif Life Robotics 21, 302–307 (2016). https://doi.org/10.1007/s10015-016-0274-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10015-016-0274-9

Keywords

Navigation