Active Control Strategies for Discovering and Localizing Devices with Range-Only Sensors

  • Benjamin CharrowEmail author
  • Nathan Michael
  • Vijay Kumar
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 107)


This paper addresses the problem of actively controlling robotic teams with range-only sensors to (a) discover and (b) localize an unknown number of devices. We develop separate information based objectives to achieve both goals, and examine ways of combining them into a unified approach. Despite the computational complexity of calculating these policies for multiple robots over long time horizons, a series of approximations enable all calculations to be performed in polynomial time. We demonstrate the tangible benefits of our approaches through a series of simulations in complex indoor environments.


Mutual Information Completion Time Gaussian Mixture Model Occupancy Grid Differential Entropy 
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.



This work was supported in part by NSF Grant 1138110 and the TerraSwarm Research Center, one of six centers supported by the STARnet phase of the Focus Center Research Program (FCRP) a Semiconductor Research Corporation program sponsored by MARCO and DARPA. The first author was supported by a NDSEG fellowship from the Department of Defense.


  1. 1.
    Rowe, A., Berges, M.E., Bhatia, G., Goldman, E., Rajkumar, R., Garrett, J.H., Moura, J.M.F., Soibelman, L.: Sensor Andrew: large-scale campus-wide sensing and actuation. IBM J. Res. Dev. 55, 6:1–6:14 (2011)CrossRefGoogle Scholar
  2. 2.
    Lazik, P., Rowe, A.: Indoor pseudo-ranging of mobile devices using ultrasonic chirps. In: ACM Conference on Embedded Network Sensor Systems, New York, USA, November 2012, p. 99 (2012)Google Scholar
  3. 3.
    Patwari, N., Ash, J., Kyperountas, S., Hero III, A., Moses, R., Correal, N.: Locating the nodes: cooperative localization in wireless sensor networks. IEEE Signal Process. Mag. 22(4), 54–69 (2005)CrossRefGoogle Scholar
  4. 4.
  5. 5.
    Hollinger, G., Sukhatme, G.: Sampling-based motion planning for robotic information gathering. In: Robotics: Science and Systems, Berlin, Germany, June 2013Google Scholar
  6. 6.
    Singh, A., Krause, A., Guestrin, C., Kaiser, W.J.: Efficient informative sensing using multiple robots. J. AI Res. 34(1), 707–755 (2009)zbMATHMathSciNetGoogle Scholar
  7. 7.
    Binney, J., Krause, A., Sukhatme, G.S.: Optimizing waypoints for monitoring spatiotemporal phenomena. Int. J. Robot. Res. 32(8), 873–888 (2013)CrossRefGoogle Scholar
  8. 8.
    Hoffmann, G., Tomlin, C.: Mobile sensor network control using mutual information methods and particle filters. IEEE Trans. Autom. Control 55(1), 32–47 (2010)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Vander Hook, J., Tokekar, P., Isler, V.: Cautious greedy strategy for bearing-only active localization: analysis and field experiments. J. Field Robot. 31(2), 296–318 (2014)CrossRefGoogle Scholar
  10. 10.
    Dames, P., Kumar, V.: Cooperative multi-target localization with noisy sensors. In: Proceedings of the IEEE International Conference on Robotics and Automation, Karlsruhe, Germany, May 2013Google Scholar
  11. 11.
    Carpin, S., Burch, D., Chung, T.H.: Searching for multiple targets using probabilistic quadtrees. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, September 2011, pp. 4536–4543 (2011)Google Scholar
  12. 12.
    Chung, T., Hollinger, G., Isler, V.: Search and pursuit-evasion in mobile robotics. Auton. Robots 31(4), 299–316 (2011)CrossRefGoogle Scholar
  13. 13.
    Charrow, B., Michael, N., Kumar, V.: Cooperative multi-robot estimation and control for radio source localization. Int. J. Robot. Res. 33(4), 569–580 (2014)CrossRefGoogle Scholar
  14. 14.
  15. 15.
    Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press, Cambridge (2008)Google Scholar
  16. 16.
    Charrow, B., Kumar, V., Michael, N.: Approximate representations for multi-robot control policies that maximize mutual information. In: Robotics: Science and Systems, Berlin, Germany, June 2013Google Scholar
  17. 17.
    Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley Online Library (2004)Google Scholar
  18. 18.
    Huber, M., Bailey, T., Durrant-Whyte, H., Hanebeck, U.: On entropy approximation for gaussian mixture random vectors. In: Multisensor Fusion and Integration for Intelligent Systems, Seoul, Korea, August 2008, pp. 181–188 (2008)Google Scholar
  19. 19.
    Bourgault, F., Makarenko, A.A., Williams, S.B., Grocholsky, B., Durrant-Whyte, H.F.: Information based adaptive robotic exploration. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots Systems (2002)Google Scholar
  20. 20.
    Bektas, T.: The multiple traveling salesman problem: an overview of formulations and solution procedures. Omega 34(3), 209–219 (2006)CrossRefGoogle Scholar
  21. 21.
    Sokkalingam, P., Aneja, Y.: Lexicographic bottleneck combinatorial problems. Oper. Res. Lett. 23(1), 27–33 (1998)CrossRefzbMATHMathSciNetGoogle Scholar
  22. 22.
    Intel Lab Occupancy Grid., September 2013

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.GRASP LaboratoryUniversity of PennsylvaniaPhiladelphiaUSA
  2. 2.Robotics InstituteCarnegie Mellon UniversityPittsburghUSA

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