An Experimental Testbed for Robotic Network Applications

  • Donato Di Paola
  • Annalisa Milella
  • Grazia Cicirelli

Abstract

In the last few years, multi-robot systems have augmented their complexity, due to the increased potential of novel sensors and actuators, and in order to satisfy the requirements of the applications they are involved into. For the development and testing of networked robotic systems, experimental testbeds are fundamental in order to verify the effectiveness of robot control methods in real contexts. In this paper, we present our Networked Robot Arena (NRA), which is a software/hardware framework for experimental testing of control and cooperation algorithms in the field of multi-robot systems. The main objective is to provide a user-friendly and flexible testbed that allows researchers and students to easily test their projects in a real-world multi-robot environment. We describe the software and hardware architecture of the NRA system and present an example of multi-mission control of a network of robots to demonstrate the effectiveness of the proposed framework.

Keywords

Mobile Robot Task Allocation Discrete Event System Experimental Testbeds Robotic Network 
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.

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References

  1. 1.
    Burgard, W., Moors, M., Stachniss, C., Schneider, F.: Coordinated multi-robot exploration. IEEE Transactions on Robotics 21(3) (2005)Google Scholar
  2. 2.
    Fierro, R., Das, A., Spletzer, J., Esposito, J., Kumar, V., Ostrowski, J.P., Pappas, G., Taylor, C.J., Hur, Y., Alur, R., Lee, I., Grudic, G., Ben Southall, B.: A Framework and Architecture for Multi-Robot Coordination. The International Journal of Robotics Research 21(10-11), 977–995 (2002)CrossRefGoogle Scholar
  3. 3.
    Kumar, V., Rus, D., Singh, S.: Robot and sensor networks for first responders. IEEE Pervasive Computing 3(4), 24–33 (2004)CrossRefGoogle Scholar
  4. 4.
    Vincent, R., Fox, D., Ko, J., Konolige, K., Limketkai, B., Morisset, B., Ortiz, C., Schulz, D., Stewart, B.: Distributed multirobot exploration, mapping, and task allocation. In: Annals of Mathematics and Artificial Intelligence. Springer, Netherlands (2009)Google Scholar
  5. 5.
    Pugh, J., Martinoli, A.: Multi-robot learning with particle swarm optimization. In: Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems (2006)Google Scholar
  6. 6.
    Lerman, K., Chris Jones, C., Galstyan, A., Matarc, M.J.: Analysis of Dynamic Task Allocation in Multi-Robot Systems. International Journal of Robotics Research 25(3), 225–241 (2006)CrossRefGoogle Scholar
  7. 7.
    Viguria, A., Maza, I., Ollero, A.: SET: An algorithm for distributed multirobot task allocation with dynamic negotiation based on task subsets. In: Proc. of 2007 IEEE International Conference on Robotics and Automation, Roma, Italy (2007)Google Scholar
  8. 8.
    Werner, F., Rckert, U., Tanoto, A., Welzel, J.: The Teleworkbench A Platform for Performing and Comparing Experiments in Robot Navigation. In: IEEE International Conference on Robotics and Automation (ICRA), Anchorage, AK, USA (2010)Google Scholar
  9. 9.
    Mondada, F., Franzi, E., Guignard, A.: The Development of Khepera. Experiments with the Mini-Robot Khepera, 7–14 (1999)Google Scholar
  10. 10.
    Mondada, F., Bonani, M., Raemy, X., Pugh, J., Cianci, C., Klaptocz, A., Magnenat, S., Zufferey, J.-C., Floreano, D., Martinoli, A.: The E-Puck, a Robot Designed for Education in Engineering. In: Proceedings of the 9th Conference on Autonomous Robot Systems and Competitions, vol. 1(1), pp. 59–65 (2009)Google Scholar
  11. 11.
    Brooks, R.A.: Intelligence without Representation. Artificial Intelligence 47, 139–159 (1991)CrossRefGoogle Scholar
  12. 12.
    Payton, D.W., Keirsey, D., Kimble, D.M., Krozel, J., Rosenblatt, J.K.: Do whatever works: A robust approach to fault-tolerant autonomous control. Applied Intelligence 2(3), 225–250 (1992)CrossRefGoogle Scholar
  13. 13.
    Tacconi, D., Lewis, F.: A new matrix model for discrete event systems: application to simulation. IEEE Control System Magazine 17(5), 62–71 (1997)CrossRefGoogle Scholar
  14. 14.
    Di Paola, D., Gasparri, A., Naso, D., Ulivi, G., Lewis, F.L.: Decentralized Task Sequencing and Multiple Mission Control for Heterogeneous Robotic Networks. In: Proc. of IEEE International Conference on Robotics and Automation (2011)Google Scholar
  15. 15.
    Di Paola, D., Naso, D., Turchiano, B.: A Heuristic Approach to Task Assignment and Control for Robotic Networks. In: Proc. of the IEEE International Symposium on Industrial Electronics, ISIE (2010)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • Donato Di Paola
    • 1
  • Annalisa Milella
    • 1
  • Grazia Cicirelli
    • 1
  1. 1.Institute of Intelligent Systems for Automation (ISSIA), National Research Council (CNR)BariItaly

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