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Adaptive Communication in Multi-robot Systems Using Directionality of Signal Strength

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

Abstract

We consider the problem of satisfying communication demands in a multi-agent system where several robots cooperate on a task and a fixed subset of the agents act as mobile routers. Our goal is to position the team of robotic routers to provide communication coverage to the remaining client robots. We allow for dynamic environments and variable client demands, thus necessitating an adaptive solution. We present an innovative method that calculates a mapping between a robot’s current position and the signal strength that it receives along each spatial direction, for its wireless links to every other robot. We show that this information can be used to design a simple positional controller that retains a quadratic structure, while capturing the behavior of wireless signals in real-world environments. Notably, our approach does not necessitate stochastic sampling along directions that are counter-productive to the overall coordination goal, nor does it require exact client positions, or a known map of the environment.

Keywords

Signal Strength Wireless Channel Synthetic Aperture Radar Disk Model Channel Feedback 
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

We thank Dan Feldman and Brian Julian for experimental and theoretical contributions to this work. The authors acknowledge MIT Lincoln Laboratory and MAST project under ARL Grant W911NF-08-2-0004 for their support.

References

  1. 1.
    Ganguli, A., S. Susca, A., Martinez, S., Bullo, F., Cortes, J.: On collective motion in sensor networks: sample problems and distributed algorithms. In: CDC-ECC (2005)Google Scholar
  2. 2.
    Jadbabaie, A., Lin, J., Morse, A.S.: Coordination of groups of mobile autonomous agents using nearest neighbor rules. In: IEEE Transactions on Automatic Control (2003)Google Scholar
  3. 3.
    Olfati-Saber, R., Fax, J.A., Murray, R.M.: Consensus and cooperation in networked multi-agent systems. In: Proceedings of the IEEE (2007)Google Scholar
  4. 4.
    MalmirChegini, M., Mostofi, Y.: On the spatial predictability of communication channels. IEEE Trans. Wirel. Commun. 11(3) (2012)Google Scholar
  5. 5.
    Yan, Y., Mostofi, Y.: Co-optimization of communication and motion planning of a robotic operation under resource constraints and in fading environments. IEEE Trans. Wirel. Commun. 12(4) (2013)Google Scholar
  6. 6.
    Fink, J., Ribeiro, A., Kumar, V.: Robust control for mobility and wireless communication in cyber-physical systems with application to robot teams. In: Proceedings of the IEEE (2012)Google Scholar
  7. 7.
    Lindh, M., Johansson, K., Bicchi, A.: An experimental study of exploiting multipath fading for robot communications. In: RSS (2007)Google Scholar
  8. 8.
  9. 9.
    Fitch, P.J.: Synthetic Aperture Radar. Springer, New York (1988)Google Scholar
  10. 10.
    Le Ny, J., Ribeiro, A., Pappas, G.J.: Adaptive communication-constrained deployment of mobile robotic networks. In: ACC (2012)Google Scholar
  11. 11.
    Spall, J.C.: Adaptive stochastic approximation by the simultaneous perturbation method. In: IEEE Transactions on Automatic Control (2000)Google Scholar
  12. 12.
    Kumar, S., Shi, L., Ahmed, N., Gil, S., Katabi, D., Rus, D.: Carspeak: a content-centric network for autonomous driving. SIGCOMM (2012)Google Scholar
  13. 13.
    Wang, J., Katabi, D.: Dude, where’s my card?. RFID positioning that works with multipath and non-line of sight, In: SIGCOMM (2013)Google Scholar
  14. 14.
    Wang, J., Adib, F., Knepper, R., Katabi, D., Daniela, R.: Robot object manipulation using rfids. In: MobiCom, RF-Compass (2013)Google Scholar
  15. 15.
    Adib, F., Katabi, D.: See through walls with wi-fi. In: SIGCOMM (2013)Google Scholar
  16. 16.
    Halperin, D., Hu, W., Sheth, A., Wetherall, D.: Predictable 802.11 packet delivery from wireless channel measurements. In: CCR (2010)Google Scholar
  17. 17.
    Rahul, H., Kumar, S.S., Katabi, D.: Scaling wireless capacity with user demand. In: SIGCOMM, Megamimo (2012)Google Scholar
  18. 18.
    Stoica, P., Moses, R.L.: Spectral Analysis of Signals. Prentice Hall, New Jersey (2005)Google Scholar
  19. 19.
    Gil, S., Feldman, D., Rus, D.: Communication coverage for independently moving robots. In: IROS (2012)Google Scholar
  20. 20.
    Feldman, D., Gil, S., Knepper, R., Julian, B., Rus, D.: K-robots clustering of moving sensors using coresets. In: ICRA 2013 (2013)Google Scholar
  21. 21.
    Halperin, D., Hu, W., Sheth, A., Wetherall, D.: Tool release: Gathering 802.11n traces with channel state information. In: ACM SIGCOMM CCR (2011)Google Scholar
  22. 22.
    Chen, H.-C., Lin, T.-H., Kung, H.T., Lin, C.-K., Gwon, Y.: Determining RF angle of arrival using cots antenna arrays: A field evaluation. In: MILCOM (2012)Google Scholar
  23. 23.
    Xiong, J., Jamieson, K.: Arraytrack: a fine-grained indoor location system. In: NSDI (2013)Google Scholar
  24. 24.
    Joshi, K., Hong, S., Katti, S.: Pinpoint: localizing interfering radios. In: NSDI (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Massachusetts Institute of TechnologyBostonUSA

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