Journal of Intelligent & Robotic Systems

, Volume 63, Issue 3–4, pp 481–501 | Cite as

Prioritized Sensor Detection for Environmental Mapping: Theory and Experiments

Article

Abstract

This paper presents a decentralized coordination algorithm that allows a team of sensor-enabled robots to navigate a region containing non-convex obstacles and take measurements within the region that contain the highest probability of having “good” information first. This approach is motivated by scenarios where prior knowledge of the search space is known or when time constraints are present that limit the amount of area that can be searched by a robot team. Our cooperative control algorithm combines Voronoi partitioning, a global optimization technique, and a modified navigation function to prioritize sensor detection. Also, we present a technique for fusing multi-sensing objectives which is accomplished through linear regression. Practical applications include search and rescue, target detection, and hazardous contaminations. The issues we address such as non-convex obstacles as well as global search are not extensively addressed in the current literature. Simulation and experimental results of the control algorithm are given, and validate the prioritized sensing behavior as well as the collision avoidance property.

Keywords

Sensor networks Cooperative control Motion planning Environmental sensing 

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References

  1. 1.
    Latombe, J.C.: Robot Motion Planning. Kluwer, Boston (1991)Google Scholar
  2. 2.
    Cortés, J., Martínez, S., Bullo, F.: Spatially-distributed converage optimization and control with limited-range interactions. In: ESIAM: Control, Optimization and Calculus of Variations, vol. 11, no. 4, pp. 691–719 (2005)Google Scholar
  3. 3.
    Lavis, B., Yokokohji, Y., Furukawa, T.: Estimation and control for cooperative autonomous searching in crowded urban emergencies. In: IEEE International Conference on Robotics and Automation, pp. 2831–2836 (2008)Google Scholar
  4. 4.
    Ferrari, S., Fierro, R., Perteet, B., Cai, C., Baumgartner, K.: A geometric optimization approach to detecting and intercepting dynamic targets using a mobile sensor network. SIAM J. Control Optim. 48(1), 292–320 (2009)MathSciNetMATHCrossRefGoogle Scholar
  5. 5.
    Cortez, R., Papageorgiou, X., Tanner, H.G., Klimenko, A.V., Borozdin, K.N., Lumia, R., Priedhorsky, W.C.: Smart radiation sensor management: nuclear search and mapping using mobile robots. IEEE Robot. Autom. Mag. 15(3), 85–93 (2008)CrossRefGoogle Scholar
  6. 6.
    Rubinstein, R.Y.: Simulation and The Monte Carlo Method. Wiley, New York (1981)MATHCrossRefGoogle Scholar
  7. 7.
    Ogren, P., Leonard, N.: A convergent dynamic window approach to obstacle avoidance. IEEE Trans. Robot. 21(2), 188–195 (2005)CrossRefGoogle Scholar
  8. 8.
    Huntwork, M., Goradia, A., Xi, N., Haffner, C., Klochko, C., Mutka, M.: Pervasive surveillance using a cooperative mobile sensor network. In: IEEE International Conference on Robotics and Automation, pp. 2099–2104 (2006)Google Scholar
  9. 9.
    Singh, A., Nowak, R., Ramanathan, P.: Active learning for adaptive sensing newtorks. In: 5th International Conference on Information Processing in Sensor Networks, pp. 60–68 (2006)Google Scholar
  10. 10.
    Oh, S., Chen, P., Manzo, M., Sastry, S.: Instumenting wireless sensor networks for real-time surveillance. In: IEEE International Conference on Robotics and Automation, pp. 3128–3111 (2006)Google Scholar
  11. 11.
    Martínez, S., Cortés, J., Bullo, F.: Motion coordination with distributed information. IEEE Control Syst. Mag. 27(4), 75–88 (2007)CrossRefGoogle Scholar
  12. 12.
    Hussein, I.I., Stipanovic, D.M.: Effective coverage control for mobile sensor networks. In: IEEE Conference on Decision and Control, pp. 2747–2752 (2006)Google Scholar
  13. 13.
    Cortez, R.A., Tanner, H.G.: Radiation mapping using multiple robots. In: 2nd ANS International Joint Topical Meeting on Emergency Preparedness and Response and Robotic and Remote Systems, pp. 157–159 (2008)Google Scholar
  14. 14.
    Tanner, H.G.: Switched UAV-UGV cooperation scheme for target detection. In: IEEE International Conference on Robotics and Automation, pp. 3457–3462 (2007)Google Scholar
  15. 15.
    Torn, A., Zilinskas, A.: Global Optimization. Springer, Berlin (1987)Google Scholar
  16. 16.
    Koditscheck, D., Rimon, E.: Robot navigation functions on monifolds with boundary. Adv. Appl. Math. 11, 412–442 (1990)CrossRefGoogle Scholar
  17. 17.
    Tanner, H.G., Kumar, A.: Towards decentralization of multi-robot navigation functions. In: IEEE International Conference on Robotics and Automation, pp. 4132–4137 (2005)Google Scholar
  18. 18.
    Loizou, S.G., Jadbabaie, A.: Density functions for navigation-function-based systems. IEEE Trans. Automat. Contr. 53(2), 612–617 (2008)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Ghaffarkhah, A., Mostofi, Y.: Communication-aware target tracking using navigation functions—centralized case. In: International Conference on Robot Communication and Coordination (ROBOCOMM), pp. 1–8 (2009)Google Scholar
  20. 20.
    Dijkstra, E.W.: A note on two problems in connexion with graphs. Numer. Math. 1, 269–271 (1959)MathSciNetMATHCrossRefGoogle Scholar
  21. 21.
    Yang, P., Freeman, R.A., Lynch, K.M.: Distributed cooperative sensing using consensus filters. In: IEEE International Conference on Robotics and Automation, pp. 405–410 (2007)Google Scholar
  22. 22.
    Cortez, R.A., Fierro, R., Wood, J.: Prioritized sensor detection via dynamic voronoi-based navigation. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’09), St. Louis, MO, October 2009, pp. 5815–5820 (2009)Google Scholar
  23. 23.
    Kvasnica, M., Grieder, P., Baotić, M.: Multi-Parametric Toolbox (MPT) (2004). Online. Available at http://control.ee.ethz.ch/~mpt/
  24. 24.
  25. 25.
  26. 26.
  27. 27.

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringUniversity of New MexicoAlbuquerqueUSA
  2. 2.Marhes Lab, Electrical & Computer Engineering DepartmentUniversity of New MexicoAlbuquerqueUSA

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