Skip to main content
Log in

Multi-agent aggregation behavior analysis: The dynamic communication topology*

  • Published:
Journal of Systems Science and Complexity Aims and scope Submit manuscript

Abstract

The authors extend the Gazi’s swarm model with local neighbor rules and the dynamic communication topology, and study its aggregation properties. Results of analysis show that all agents in the models aggregate and eventually form a cohesive cluster of finite size around the swarm center or the appointed point. Finally, simulations are provided to testify some of the results. Models in the paper are more applicable to the reality for the advantage that each agent only needs the partial information of the entire dynamic system when making motion decision.

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.

Similar content being viewed by others

References

  1. T. Chu, L. Wang, and T. Chen, Self-organized motion in anisotropic swarms, Journal of Control Theory and Applications, 2003, 1(1): 77–81.

    Article  Google Scholar 

  2. L. Wang, H. Shi, T. Chu, W. Zhang, and L. Zhang, Aggregation of forging swarms, in Advances in Artifial Intelligence, Lecture Notes in Artifial Intelligence, Spriger-Verlag, Berlin, 2004, 766–777.

  3. T. Chu, L. Wang, and T. Chen, Self-organized motion in a class of anisotropic swarms: convergence vs. oscillation, in Proceedings of American Control Conference, Portland, Oregon, USA, 2005, 3474–3479.

  4. V. Gazi and K. M. Passino, Stability analysis of swarms, IEEE Transactions on Automatic Control, 2003, 48(4): 692–697.

    Article  Google Scholar 

  5. V. Gazi and K. M. Passino, A class of attraction/repulsion functions for stable swarm aggregations, International Journal of Control, 2004, 77(18): 1567–1579.

    Article  Google Scholar 

  6. V. Gazi and K. M. Passino, Stability analysis of social foraging swarms, IEEE Transaction Systems, Man, and Cybernetics-Part B: Cybernetics, 2004, 34(1): 539–557.

    Article  Google Scholar 

  7. V. Gazi and K. M. Passino, Stable social foraging swarms in a noisy environment, IEEE Transactions on Automatic Control, 2004, 49(1): 30–44.

    Article  Google Scholar 

  8. Y. Hong, J. Hu, and L. Gao, Tracking control for multi-agent consensus with an active leader and variable topology, Automatica, 2006, 42(7): 1177–1182.

    Article  Google Scholar 

  9. A. Jadbabaie, J. Lin, and A. S. Morse, Coordination of groups of mobile autonomous agents using nearest neighbor rules, IEEE Transactions on Automatic Control, 2003, 48(6): 988–1001.

    Article  Google Scholar 

  10. P. Ogren, E. Fiorelli, and E. L. Naomi, Cooperative control of mobile sensor networks: Adaptive gradient climbing in a distributed environment, IEEE Transactions on Automatic Control, 2004, 49(8): 1292–1302.

    Article  Google Scholar 

  11. R. Olfati-Saber and R. M. Murray, Consensus problems in networks of agents with switching topology and time-delays, IEEE Transactions on Automatic Control, 2004, 49(9): 1520–1533.

    Article  Google Scholar 

  12. W. Ren and R. W. Beard, Consensus seeking in multi-agent systems under dynamically changing interaction topologies, IEEE Transactions on Automatic Control, 2005, 50(5): 655–661.

    Article  Google Scholar 

  13. H. Shi, L. Wang, and T. Chu, Virtual leader approach to coordinated control of mutiple mobile agents with asymmetric interactions, Physica D, 2006, 213(1): 51–65.

    Article  Google Scholar 

  14. C. Yaoli, R. Maria, M. Daniel, L. B. Andrea, and S. C. Lincoln, State transitions and the continuum limit for a 2D interacting, self-propelled particle system, Physica D, 2007, 232(1): 33–47.

    Article  Google Scholar 

  15. C. Godsil and G. Royle, Algebraic Graph Theory, Springer-Verlag, New York, 2001.

  16. D. Zhu, C. Wu, and W. Qin, Multivariate Statistical Analysis and SAS Software, Southeast University Press, Nanjing, 1999.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sheng CHEN.

Additional information

*This research is supported by Project Operation and Simulation of Emergency Response Logistics Network in the System of Anti-bioterrorism supported by the National Natural Science Foundation of China under Grant No. 70671021.

Rights and permissions

Reprints and permissions

About this article

Cite this article

CHEN, S., ZHAO, L. & HAN, Y. Multi-agent aggregation behavior analysis: The dynamic communication topology*. J Syst Sci Complex 21, 209–216 (2008). https://doi.org/10.1007/s11424-008-9104-z

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11424-008-9104-z

Key words

Navigation