Global Reconfiguration of a Team of Networked Mobile Robots Among Obstacles

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

Abstract

This paper presents a full system demonstration of dynamic sensor-based reconfiguration of a networked robot team. Robots sense obstacles in their environment locally and dynamically adapt their global geometric configuration to conform to an abstract goal shape. We present a novel two-layer planning and control algorithm for team reconfiguration that is decentralised and assumes local (neighbour-to-neighbour) communication only. The approach is designed to be resource-efficient and we show experiments using a team of nine mobile robots with modest computation, communication and sensing. The robots use acoustic beacons for localisation and can sense obstacles in their local neighbourhood using IR sensors. Our results demonstrate globally specified reconfiguration from local information in a real robot network, and highlight limitations of standard mesh networks in implementing decentralised algorithms.

Keywords

Team reconfiguration Networked robots Multi-robot systems  Decentralised navigation function 

Notes

Acknowledgments

This work was supported in part by the Australian Centre for Field Robotics (ACFR) and the NSW State Government.

References

  1. 1.
    Stewart, R.L., Russell, R.A.: A distributed feedback mechanism to regulate wall construction by a robotic swarm. Adapt. Behav. 14(1) (2006) 21–51Google Scholar
  2. 2.
    Ferrante, E., Turgut, A.E., Huepe, C., Stranieri, A., Pinciroli, C., Dorigo, M.: Self-organized flocking with a mobile robot swarm: a novel motion control method. Adapt. Behav. 20(6) (2012) 460–477Google Scholar
  3. 3.
    Theraulaz, G., Bonabeau, E.: Modelling the collective building of complex architectures in social insects with lattice swarms. J. Theor. Biol. 177(4) (1995) 381–400Google Scholar
  4. 4.
    Fitch, R., Butler, Z.: Million module march: Scalable locomotion for large self-reconfiguring robots. Int. J. Rob. Res. 27(3–4) (2008) 331–343Google Scholar
  5. 5.
    Michael, N., Kumar, V.: Planning and control of ensembles of robots with nonholonomic constraints.Int. J. Rob. Res. 28(8) (August 2009) 962–975CrossRefGoogle Scholar
  6. 6.
    Liu, L., Shell, D.A.: Physically routing robots in a multi-robot network: Flexibility through a three-dimensional matching graph. Int. J. Rob. Res. 32(12) (2013) 1475–1494Google Scholar
  7. 7.
    Yim, M., Shen, W.M., Salemi, B., Rus, D., Moll, M., Lipson, H., Klavins, E., Chirikjian, G.S.: Modular self-reconfigurable robot systems: Challenges and opportunties for the future. IEEE Robot. Automat. Mag. 14(1) (March 2007) 43–52CrossRefGoogle Scholar
  8. 8.
    Fitch, R., Rus, D.: Self-reconfiguring robots in the USA. Journal of the Robotics Society of Japan 21(8) (Nov. 2003) 4–10CrossRefGoogle Scholar
  9. 9.
    Butler, Z., Fitch, R., Rus, D.: Experiments in distributed control for modular robots. In: Experimental Robotics VIII. Springer (2003) 307–316Google Scholar
  10. 10.
    Zelinski, S., Koo, T.J., Sastry, S.: Optimization-based formation reconfiguration planning for autonomous vehicles. In: Proc. of IEEE/ICRA. (2003) 3758–3763Google Scholar
  11. 11.
    Miklic, D., Bogdan, S., Fierro, R.: Decentralized grid-based algorithms for formation reconfiguration and synchronization. In: Proc. of IEEE/ICRA. (2010) 4463–4468Google Scholar
  12. 12.
    Murray, R.: Recent research in cooperative control of multivehicle systems. J. Dyn. Sys., Meas., Control 129(5) (2007) 571–583Google Scholar
  13. 13.
    Balch, T., Arkin, R.: Behavior-based formation control for multirobot teams. IEEE Trans. Robot. Automat. 14(6) (Dec 1998) 926–939CrossRefGoogle Scholar
  14. 14.
    Das, A.K., Fierro, R., Kumar, V., Ostrowski, J.P., Spletzer, J., Taylor, C.J.: A vision-based formation control framework. IEEE Trans. Robot. Automat. 18(5) (2002) 813–825Google Scholar
  15. 15.
    Olfati-Saber, R., Murray, R.: Distributed cooperative control of multiple vehicle formations using structural potential functions. In: Proc. of IFAC World Congress. (2002)Google Scholar
  16. 16.
    Tanner, H., Kumar, A.: Formation stabilization of multiple agents using decentralized navigation functions. In: Proc. of Robotics: Science and Systems. (2005)Google Scholar
  17. 17.
    Kress-Gazit, H., Wongpiromsarn, T., Topcu, U.: Correct, reactive, high-level robot control. IEEE Rob. Autom. Mag. 18(3) (2011) 65–74Google Scholar
  18. 18.
    Fitch, R., Lal, R.: Experiments with a ZigBee wireless communication system for self-reconfiguring modular robots. In: Proc. of IEEE ICRA. (2009) 1947–1952Google Scholar
  19. 19.
    Sutton, R.S., Barto, A.G.: Introduction to reinforcement learning. MIT Press (1998)Google Scholar
  20. 20.
    Littman, M., Dean, T., Kaelbling, L.P.: On the complexity of solving markov decision problems. In: Proc. of UAI. (1995) 394–402Google Scholar
  21. 21.
    Dumitrescu, A., Pach, J.: Pushing squares around. In: Proc. of the Symposium on Computational Geometry. (2004) 116–123Google Scholar
  22. 22.
    Chan, Y.T., Ho, K.C.: A simple and efficient estimator for hyperbolic location. IEEE Trans. Signal Processing 42(8) (1994) 1905–1915Google Scholar
  23. 23.
    Kuo, V.: Enabling Parallel Wireless Communication in Mobile Robot Teams. PhD thesis, The University of Sydney (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Robert Fitch
    • 1
  • Alen Alempijevic
    • 2
  • Thierry Peynot
    • 1
    • 3
  1. 1.Australian Centre for Field RoboticsThe University of SydneySydneyAustralia
  2. 2.University of Technology SydneySydneyAustralia
  3. 3.School of Electrical Engineering and Computer ScienceQueensland University of TechnologyBrisbaneAustralia

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