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Coverage with a Team of Wheeled Mobile Robots

  • C. A. Rabbath
  • N. Léchevin
Article

Abstract

This article presents a control system enabling coverage of a prescribed ground area by a team of wheeled mobile robots. The control system relies on equal balancing of the costs among the wheeled robots. Cost balancing leverages the intuition that a healthy neighbor could help a degraded robot to carry out its monitoring task simply by moving slightly towards the degraded robot. This feature allows the control system to support situations where vehicles have varying sensor characteristics. The article focuses on the design of the system and the results of experiments obtained with a small number of networked ground robots. The coverage control system is decentralized. Hence, no element of the system is a single point of failure, and the computations can be distributed. The experiments show (1) the satisfactory, yet suboptimal, performance of the coverage control system under healthy conditions, and the adaptation of the vehicles in case a team member is subject to a degraded condition of operation, and (2) the feasibility of the integration of the coverage control system with low-cost commercial wheeled mobile robot systems.

Keywords

Coverage Voronoi coverage Optimization Wheeled mobile robots Experiments Cooperative control Health management 

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References

  1. 1.
    Rabbath, C.A., Léchevin, N.: Safety and Reliability in Cooperating Unmanned Aerial Systems. World Scientific Publishing (2010)Google Scholar
  2. 2.
    Smith, S.L., Schwager, M., Rus, D.: Persistent tasks for robots in changing environments. IEEE Transactions on Robotics, pp. 1–14 (2010)Google Scholar
  3. 3.
    Cortés, 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.
    Cortes, J., Martinez, S., Bullo, F.: Spatially-distributed coverage optimization and control with limited-range interactions. ESAIM: Control Optimisation Calc Var 11(4), 691–719 (2005)CrossRefzbMATHMathSciNetGoogle Scholar
  5. 5.
    Stergiopoulos, Y., Tzes, A.: Convex voronoi-inspired space partitioning for heterogeneous networks: a coverage-oriented approach. Control Theory Appl. IET 4(12), 2802–2812 (2010)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Schwager, M., Julian, B., Angermann, M., Rus, D.: Eyes in the sky: decentralized control for the deployment of robotic camera networks. In: Proceedings of the IEEE (2010)Google Scholar
  7. 7.
    Wang, Y., Hussein, I.I.: Awareness coverage control over large-scale domains with intermittent communications. IEEE Trans. Autom. Control 55(8), 1850–1859 (2010). doi: 10.1109/TAC.2010.2042346 CrossRefMathSciNetGoogle Scholar
  8. 8.
    Marier, J.-S., Rabbath, C.-A., Léchevin, N.: Optimizing the location of sensors subject to health degradation. In: American Control Conference, pp. 3760–3765 (2011)Google Scholar
  9. 9.
    Marier, J.-S., Rabbath, C.A., Léchevin, N.: Placement of a team of surveillance vehicles subject to navigation failures. In: AIAA Guidance, Navigation, and Control Conference, pp. AIAA 2011–6476. AIAA (2011)Google Scholar
  10. 10.
    Suzuki, A., Drezner, Z.: The p-center location problem in an area. Locat. Sci. 4(1–2), 69–82 (1996)CrossRefzbMATHGoogle Scholar
  11. 11.
    Aurenhammer, F.: Voronoi diagrams—a survey of a fundamental geometric data structure. ACM Comput. Surv. 23(3), 345–405 (1991)CrossRefGoogle Scholar
  12. 12.
    Aurenhammer, F.: Power diagrams: properties, algorithms and applications. SIAM J. Comput. 16(1), 78–96 (1987)CrossRefzbMATHMathSciNetGoogle Scholar
  13. 13.
    Cortés, J.: Coverage optimization and spatial load balancing by robotic sensor networks. IEEE Trans. Autom. Control 55(3), 749–754 (2010)CrossRefGoogle Scholar
  14. 14.
    Pimenta, L., Schwager, M., Lindsey, Q., Kumar, V., Rus, D., Mesquita, R., Pereira, G.: Simultaneous coverage and tracking (SCAT) of moving targets with robot networks. Algorithmic Foundation of Robotics VIII, pp. 85–99 (2009)Google Scholar
  15. 15.
    Schwager, M.: A gradient optimization approach to adaptive multi-robot control. Ph.D. thesis, Massachusetts Institute of Technology (2009)Google Scholar
  16. 16.
    Lloyd, S.: Least squares quantization in PCM. IEEE Trans. Inf. Theory 28(2), 129–137 (1982)CrossRefzbMATHMathSciNetGoogle Scholar
  17. 17.
    Olfati-Saber, R., Fax, J.A., Murray, R.M.: Consensus and cooperation in networked multi-agent systems. Proc. IEEE 95(1), 215–233 (2007)CrossRefGoogle Scholar
  18. 18.
    Hines, W.W., Montgomery, D.C.: Probability and Statistics in Engineering and Management Science. Wiley, New York (1980)Google Scholar
  19. 19.
  20. 20.
    Koditschek, D.E., Rimon, E.: Robot navigation functions on manifolds with boundary. Adv. Appl. Math. 11(4), 412–442 (1990)CrossRefzbMATHMathSciNetGoogle Scholar
  21. 21.
    NaturalPoint, Inc.: Optitrack flex:v100r2 (2011). http://www.naturalpoint.com/optitrack/products/flex-v100r2/. Accessed 17 June 2011
  22. 22.
    Quanser Consulting Inc.: Quanser – control solutions – control design software quarcⒸ2.1. http://www.quanser.com/english/html/solutions/fs_soln_software.html. Accessed 17 June 2011 (2011)
  23. 23.
    Gumstix Inc.: http://www.gumstix.com/. Accessed 18 July 2012 (2012)

Copyright information

© Her Majesty the Queen in Right of Canada 2014

Authors and Affiliations

  1. 1.DRDC Valcartier Research CentreQuebec CityCanada

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