Cooperative Multi-robot Estimation and Control for Radio Source Localization

  • Benjamin CharrowEmail author
  • Nathan Michael
  • Vijay Kumar
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 88)


We develop algorithms for estimation and control that allow a team of robots equipped with range sensors to localize an unknown target in a known but complex environment. We present an experimental model for radio-based time-of-flight range sensors. Adopting a Bayesian approach for estimation, we then develop a control law which maximizes the mutual information between the robot’s measurements and their current belief of the target position. We describe experimental results for a robot team localizing a stationary target in several representative indoor environments in which the unknown target is reliably localized with an error well below the typical error for individual measurements.


Mutual Information Gaussian Mixture Model Range Sensor Cooperative Localization Mobile Beacon 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley Online Library (2004)Google Scholar
  2. 2.
    Djugash, J., Singh, S.: Motion-aided network slam. In: Proc. of the Intl. Sym. on Exp. Robot., New Dehli and Agra, India (December 2010)Google Scholar
  3. 3.
    Djugash, J., Singh, S., Kantor, G., Zhang, W.: Range-only SLAM for robots operating cooperatively with sensor networks. In: Proc. of the IEEE Intl. Conf. on Robot. and Autom., Orlando, USA, pp. 2078–2084 (2006)Google Scholar
  4. 4.
    Grocholsky, B.: Information-theoretic control of multiple sensor platforms. PhD thesis, University of Sydney, Sydney, Australia (2002)Google Scholar
  5. 5.
    Hoffmann, G.M., Tomlin, C.J.: Mobile sensor network control using mutual information methods and particle filters. IEEE Trans. Autom. Control 55(1), 32–47 (2010)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Huber, M.F., Bailey, T., Durrant-Whyte, H., Hanebeck, U.D.: On entropy approximation for gaussian mixture random vectors. In: Multisensor Fusion and Integration for Intelligent Systems, Seoul, Korea, pp. 181–188 (2008)Google Scholar
  7. 7.
    Jourdan, D., Deyst, J., Win, M., Roy, N.: Monte carlo localization in dense multipath environments using UWB ranging. In: IEEE Intl. Conf. on Ultra-Wideband, Zurich, Switzerland, pp. 314–319 (September 2005)Google Scholar
  8. 8.
    Kantor, G., Singh, S.: Preliminary results in range-only localization and mapping. In: Proc. of the IEEE Intl. Conf. on Robot. and Autom., Washington, D.C., vol. 2, pp. 1818–1823 (2002)Google Scholar
  9. 9.
    Morelli, C., Nicole, M., Rampa, V., Spagnolini, U.: Hidden markov models for radio localization in mixed LOS/NLOS conditions. IEEE Trans. Signal Process. 55(4), 1525–1542 (2007)MathSciNetCrossRefGoogle Scholar
  10. 10.
    nanoPAN 5375 Development Kit (February 2012),
  11. 11.
    Olson, E., Leonard, J.J., Teller, S.: Robust range-only beacon localization. IEEE J. Oceanic Eng. 31(4), 949–958 (2006)CrossRefGoogle Scholar
  12. 12.
    Patwari, N., Ash, J.N., Kyperountas, S., Hero III, A.O., Moses, R.L., Correal, N.S.: Locating the nodes: cooperative localization in wireless sensor networks. IEEE Signal Process. Mag. 22(4), 54–69 (2005)CrossRefGoogle Scholar
  13. 13.
    Rappaport, T.S.: Wireless Communications: Principles and Practice. Prentice Hall (1996)Google Scholar
  14. 14.
    Robot Operating System (February 2012),
  15. 15.
    RVO2 Library (February 2012),
  16. 16.
    Ryan, A., Hedrick, J.K.: Particle filter based information-theoretic active sensing. Robotics and Autonomous Systems 58(5), 574–584 (2010)CrossRefGoogle Scholar
  17. 17.
    Sadler, B.M., Liu, N., Xu, Z., Kozick, R.: Range-based geolocation in fading environments. In: Allerton Conf. on Comm., Control, and Comput., Allerton House, USA, pp. 15–20 (2008)Google Scholar
  18. 18.
    Snape, J., van den Berg, J., Guy, S.J., Manocha, D.: Smooth and collision-free navigation for multiple robots under differential-drive constraints. In: Proc. of the IEEE/RSJ Intl. Conf. on Intell. Robots and Syst., Anchorage, USA, pp. 4584–4589 (2010)Google Scholar
  19. 19.
    Spletzer, J., Taylor, C.J.: A bounded uncertainty approach to multi-robot localization. In: Proc. of the IEEE/RSJ Intl. Conf. on Intell. Robots and Syst., Las Vegas, USA, vol. 2, pp. 1258–1265 (October 2003)Google Scholar
  20. 20.
    Stump, E., Kumar, V., Grocholsky, B., Shiroma, P.M.: Control for localization of targets using range-only sensors. Intl. J. Robot. Research 28(6), 743–757 (2009)CrossRefGoogle Scholar
  21. 21.
    Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press (2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  1. 1.GRASP LabUniversity of PennsylvaniaPhiladelphiaUSA

Personalised recommendations