Skip to main content

A Scalable, Decentralised Large-Scale Network of Mobile Robots for Multi-target Tracking

  • Conference paper
  • First Online:
Intelligent Autonomous Systems 13

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 302))

Abstract

A scalable, decentralised large-scale network of mobile robots for multi-target tracking is addressed in this paper. The decentralised control is originally built up by behavioural control but upgraded with decentralised robot control for connectivity maintenance and decentralised connectivity control for hierarchical connectivity removal, allowing the network expansion for tracking and occupying spatially distributed targets. The multi-target tracking algorithm guarantees that the mobile robots reach targets at very high efficiency, while at least an interconnectivity network connecting all the mobile robots is preserved for information exchange. The Monte Carlo simulation results illustrate characteristics of the decentralised control as well as its scalability through several experimental scenarios.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 349.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 449.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Reynolds, C. W., Flocks, Herds, and Schools: A Distributed Behavioral Model. Computer Graphics, 21(4), pp. 25–34, 1987.

    Google Scholar 

  2. Mataric, M.J., Designing and Understanding adaptive group behaviors. Adaptive Behavior, Vol. 4, pp. 51–80, 1995.

    Google Scholar 

  3. Khatib, O., Real-time Obstacle Avoidance for Manipulators and Mobile Robots. Int. J. Rob. Res., 1986, 5, 90–99.

    Google Scholar 

  4. Elkaim, G.; Siegel, M. A., Lightweight Control Methodology for Formation Control of Vehicle Swarms. In Proceedings of the 16th IFAC World Congress, Prague, Czech Republic, 4–8 July 2005.

    Google Scholar 

  5. Reif, J.; Wang, H., Social potential fields: A Distributed Behavioral Control for Autonomous Robots. Rob. Autonomous Syst., 1999, 27, 171–194.

    Google Scholar 

  6. Spears, W., Spears, D., Hamann, J. Heil, R. Distributed, Physics-based Control of Swarms of Vehicles. Autonomous Robot., 2004, 17, 137–162.

    Google Scholar 

  7. Ge, S.S., Cui, Y.J., New Potential Functions for Mobile Robot Path Planning. IEEE Trans. Rob. Autom., 2000, 16, 615–620.

    Google Scholar 

  8. Kim, H.D.; Shin, S., Wang, O.H., Decentralized Control of Autonomous Swarm Systems, Using Artificial Potential Functions: Analytical Design Guidelines. Int. J. Intell. Rob. Syst., 2006, 45, 369–394.

    Google Scholar 

  9. Horward, A., Mataric, M., Sukatme, G., Mobile Sensor Network Deployment using Potential Fields: A Distributed, Scalable Solution to the Area Coverage Problem. In Proceedings of the Sixth International Symposium on Distributed Autonomous Robotics Systems, Fukuoka, Japan, 25–27 June 2002; pp. 229-208.

    Google Scholar 

  10. Mikkelsen, B.S., Jespersen, R., Ngo, T.D., Probabilistic Communication based Potential Force for Robot Formations: A Practical Approach. In Springer Tracts in Advanced Robotics, Vol 83, 2013, pp 243–253.

    Google Scholar 

  11. Tanner, G.H., Jadbabai, A., Pappas, J.G., Stable Flocking of Mobile Agents, Part I: Fixed Topology. In Proceedings of the 42nd IEEE Conference on Decision and Control, Maui, HI, USA, 12 December 2003; pp. 2010–2015.

    Google Scholar 

  12. Tanner, G.H., Jadbabai, A., Pappas, J.G., Stable Flocking of Mobile Agents, Part II: Dynamic Topology. In Proceedings of the 42nd IEEE Conference on Decision and Control, Maui, HI, USA, 12 December 2003; pp. 2016–2021.

    Google Scholar 

  13. Desai, P.J., A Graph Theoretic Approach for Modelling Mobile Robot Team Formations. J. Rob. Syst., 2002, 19, 511–525.

    Google Scholar 

  14. Dong, W., Guo, Y., Formation Control of Nonholonomic Mobile Robots using Graph Theoretical Methods. Lect. Notes Econ. Math. Syst., 2007, 588, pp. 369–386.

    Google Scholar 

  15. Ji, M., Egerstedt, M. Distributed Coordination Control of Multi-agent Systems while Preserving Connectedness. IEEE Trans. Rob., 2007, 23, pp. 693–703.

    Google Scholar 

  16. Olfati-Saber, R. Murray, M.R., Consensus Problems in Networks of Agents with Switching Topology and Time-delays. IEEE Trans. Autom. Control, 49, pp. 1520–1533.

    Google Scholar 

  17. D. V. Dimarogonas and K. J. Kyriakopoulos, ÒConnectedness preserving distributed swarm aggregation for multiple kinematic robots.Ò IEEE Transactions on Robotics, vol. 24, no. 5, pp. 1213–1223, 2008.

    Google Scholar 

  18. L. Blazovics, K. Crorba, B. Forstner, and C. Hassan, Target tracking and surrounding with swarm robots, Conference and Workshops on Engineering of Computer-Based Systems, pp. 135–141, 2012.

    Google Scholar 

  19. B. Jung, and G. S. Sukhatme, Tracking Targets using Multiple Robots: The Effect of Environment Occlusion, Autonomous Robots Journal, Vol. 13, No. 3, pp. 191–205, 2002.

    Google Scholar 

  20. B. Shucker, and J. K. Bennett, Target Tracking with Distributed Robotic Macrosensors, Military Communications Conference (MILCOM), Vol. 4, pp. 2617–2623, 2005.

    Google Scholar 

  21. L. Parker, Distributed Algorithms for Multiple Observation of Multiple Moving Targets, Autonomous Robots, Vol. 12(3), pp 231–255, 2002.

    Google Scholar 

  22. La H.M., Sheng W., Dynamic target tracking and observing in a mobile sensor network, in Robotics and Autonomous Systems 60(2012) 996–2009.

    Google Scholar 

  23. Istvan H., Krzysztof S., Robot team coordination for target tracking usig fuzzy logic controller in game theoretic framework, in Robotics and Autonomous System 57(2009) 75–86.

    Google Scholar 

  24. David P., Rui P. Rocha, Distributed multi-robot patrol: A Scalable and fault-tolerent framework, in Robotics and Autonomous Systems 61(2013) 1572–1587.

    Google Scholar 

  25. Pham.H.D., Pham.M.T, Tran.Q.V, Ngo. T.D., Accelerating Multi-Target Tracking by a Swarm of Mobile Robots with Network Preservation, in Proceedings of International Conference of Soft Computing and Pattern Recognition, 2013, December, Hanoi, Vietnam, pp. 327–332.

    Google Scholar 

Download references

Acknowledgments

This research was supported in part by the University of Brunei Darrusalam (UBD/PNC2/2/RG/1(259)) and the Asia Research Centre and the Korea Foundation for Advanced Studies.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Trung Dung Ngo .

Editor information

Editors and Affiliations

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (rtf 1 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Hung, P.D., Vinh, T.Q., Ngo, T.D. (2016). A Scalable, Decentralised Large-Scale Network of Mobile Robots for Multi-target Tracking. In: Menegatti, E., Michael, N., Berns, K., Yamaguchi, H. (eds) Intelligent Autonomous Systems 13. Advances in Intelligent Systems and Computing, vol 302. Springer, Cham. https://doi.org/10.1007/978-3-319-08338-4_46

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-08338-4_46

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08337-7

  • Online ISBN: 978-3-319-08338-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics