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Adaptive Inter-Robot Trust for Robust Multi-Robot Sensor Coverage

Chapter
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 114)

Abstract

This paper proposes a new approach to both characterize inter-robot trust in multi-robot systems and adapt trust online in response to the relative performance of the robots. The approach is applied to a multi-robot coverage control scenario, in which a team of robots must spread out over an environment to provide sensing coverage. A decentralized algorithm is designed to control the positions of the robots, while simultaneously adapting their trust weightings. Robots with higher quality sensors take charge of a larger region in the environment, while robots with lower quality sensors have their regions reduced. Using a Lyapunov-type proof, it is proven that the robots converge to locally optimal positions for sensing that are as good as if the robots’ sensor qualities were known beforehand. The algorithm is demonstrated in Matlab simulations.

Keywords

Cost Function Voronoi Cell Adaptive Weighting Trust Weighting Coverage Control 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was supported in part by ONR grant N00014-12-1-1000, and by a Clare Boothe Luce Fellowship. We are grateful for this financial support.

References

  1. 1.
    Breitenmoser, A., Schwager, M., Metzger, J.C., Siegwart, R., Rus, D.: Voronoi coverage of non-convex environments with a group of networked robots. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 4982–4989. IEEE (2010)Google Scholar
  2. 2.
    Cortés, J.: Coverage optimization and spatial load balancing by robotic sensor networks. IEEE Trans. Autom. Control 55(3), 749–754 (2010)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Cortes, J., Martinez, S., Karatas, T., Bullo, F.: Coverage control for mobile sensing networks. IEEE Trans. Robot. Autom. 20(2), 243–255 (2004)CrossRefGoogle Scholar
  4. 4.
    Drezner, Z.: Facility Location: A Survey of Applications and Methods. Springer Series in Operations Research. Springer, New York (1995)CrossRefGoogle Scholar
  5. 5.
    Du, Q., Faber, V., Gunzburger, M.: Centroidal Voronoi tessellations: applications and algorithms. SIAM Rev. 41(4), 637–676 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Godsil, C., Royle, G.: Algebraic Graph Theory. Graduate Texts in Mathematics. Springer, New York (2001)CrossRefzbMATHGoogle Scholar
  7. 7.
    Horn, R.A., Johnson, C.R.: Matrix Analysis. Cambridge University Press, Cambridge (1990)zbMATHGoogle Scholar
  8. 8.
    Khalil, H.: Nonlinear Systems. Prentice Hall PTR, Upper Saddle River (2002)zbMATHGoogle Scholar
  9. 9.
    Kwok, A., Martinez, S.: Energy-balancing cooperative strategies for sensor deployment. In: 46th IEEE Conference on Decision and Control, pp. 6136–6141. IEEE (2007)Google Scholar
  10. 10.
    Marier, J.S., Rabbath, C.A., Léchevin, N.: Optimizing the location of sensors subject to health degradation. In: Proceedings of the American Control Conference (ACC), pp. 3760–3765. IEEE (2011)Google Scholar
  11. 11.
    Marier, J.S., Rabbath, C.A., Léchevin, N.: Health-aware coverage control with application to a team of small UAVs. IEEE Trans. Control Sys. Technol. 21, 1719–1730 (2012)CrossRefGoogle Scholar
  12. 12.
    Pavone, M., Arsie, A., Frazzoli, E., Bullo, F.: Equitable partitioning policies for robotic networks. In: IEEE International Conference on Robotics and Automation, ICRA’09, pp. 2356–2361. IEEE (2009)Google Scholar
  13. 13.
    Pimenta, L., Kumar, V., Mesquita, R.C., Pereira, G.: Sensing and coverage for a network of heterogeneous robots. In: 47th IEEE Conference on Decision and Control, CDC 2008, pp. 3947–3952. IEEE (2008)Google Scholar
  14. 14.
    Salapaka, S., Khalak, A., Dahleh, M.: Constraints on locational optimization problems. In: Proceedings of the 42nd IEEE Conference on Decision and Control, vol. 2, pp. 1741–1746. IEEE (2003)Google Scholar
  15. 15.
    Schwager, M., Bullo, F., Skelly, D., Rus, D.: A ladybug exploration strategy for distributed adaptive coverage control. In: IEEE International Conference on Robotics and Automation, ICRA 2008, pp. 2346–2353. IEEE (2008)Google Scholar
  16. 16.
    Schwager, M., Rus, D., Slotine, J.J.: Decentralized, adaptive coverage control for networked robots. Int. J. Robot. Res. 28(3), 357–375 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Boston UniversityBostonUSA
  2. 2.Stanford UniversityStanfordUSA

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