Measuring Variables Effect to Statistically Model the Multi-Robot Patrolling Problem by Means of ANOVA

  • David Portugal
  • Rui P. Rocha
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 372)

Abstract

This paper focuses on analyzing extensive results generated from running diverse multi-robot patrolling algorithms with different configurations towards measuring the influence of the variables of the general problem. In order to do this, a statistical technique by the name of Analysis of Variance (ANOVA) is employed to compare the parameters and identify the ones which give raise to the total dispersion of the data set, at the same time accessing their contribution to the obtained results. It is shown that by applying such technique, it is possible to compute a data-related model for the Multi-Robot Patrolling Problem (MRPP).

Keywords

Multi-Robot Patrolling ANOVA and Statistical Analysis 

References

  1. 1.
    Portugal, D., Rocha, R.P.: On the Performance and Scalability of Multi-Robot Patrolling Algorithms. In: Proc. of the 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR 2011), Kyoto, Japan, November 1-5, pp. 50–55 (2011)Google Scholar
  2. 2.
    ROS.org: Stage stack, http://www.ros.org/wiki/stage
  3. 3.
    Machado, A., Ramalho, G., Zucker, J., Drogoul, A.: Multi-Agent Patrolling: an Empirical Analysis of Alternative Architectures. In: 3rd Int. Workshop on Multi-Agent-Based Simulation, Italy, pp. 155–170 (July 2002)Google Scholar
  4. 4.
    Almeida, A., Ramalho, G., Sanana, H., Tedesco, P., Menezes, T., Corruble, V., Chaveleyre, Y.: Recent Advances on Multi-agent Patrolling. In: Bazzan, A.L.C., Labidi, S. (eds.) SBIA 2004. LNCS (LNAI), vol. 3171, pp. 474–483. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. 5.
    Portugal, D., Rocha, R.P.: MSP Algorithm: Multi-Robot Patrolling based on Territory Allocation using Balanced Graph Partitioning. In: Proc. of the 25th ACM Symposium on Applied Computing (SAC 2010), Sierre, Switzerland, March 22-26, pp. 1271–1276 (2010)Google Scholar
  6. 6.
    Chevaleyre, Y.: Theoretical Analysis of the Multi-agent Patrolling Problem. In: IEEE/WIC/ACM Int. Conf. Proc. of the Intelligent Agent Technology (IAT 2004), Beijing, China, September 20-24, pp. 302–308 (2004)Google Scholar
  7. 7.
    Fazli, P., Davoodi, A., Pasquier, P., Mackworth, A.K.: Complete and Robust Cooperative Robot Area Coverage with Limited Range. In: Proc. of the IEEE/RSJ Int. Conference on Intelligent Robots and Systems (IROS 2010), Taipei, Taiwan (2010)Google Scholar
  8. 8.
    Sempé, F., Drogoul, A.: Adaptive Patrol for a Group of Robots. In: Proc. of the Int. Conf. on Intelligent Robots and Systems (IROS 2003), Las Vegas, Nevada (October 2003)Google Scholar
  9. 9.
    Menezes, T., Azevedo Tedesco, P., Ramalho, G.L.: Negotiator Agents for the Patrolling Task. In: Sichman, J.S., Coelho, H., Rezende, S.O. (eds.) IBERAMIA 2006 and SBIA 2006. LNCS (LNAI), vol. 4140, pp. 48–57. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  10. 10.
    Chu, H., Glad, A., Simonin, O., Sempé, F., Drogoul, A., Charpillet, F.: Swarm Approaches for the Patrolling Problem, Information Propagation vs. Pheromone Evaporation. In: Proc. of the International Conference on Tools with Art. Intelligence, France, pp. 442–449 (2007)Google Scholar
  11. 11.
    Guo, Y., Parker, L., Madhavan, R.: Collaborative Robots for Infrastructure Security Applications. SCI, vol. 50, pp. 185–200. Springer, Heidelberg (2007)Google Scholar
  12. 12.
    N. Basilico, N. Gatti, T. Rossi, S. Ceppi and F. Amigoni, “Extending Algorithms for Mobile Robot Patrolling in the Presence of Adversaries to More Realistic Settings”. In Proc. of WI-IAT 2009 IEEE/WIC/ACM Int. Joint Conference on Web Intelligence and Intelligent Agent Technology, IEEE Press, Vol. 02, pp. 557-564, Milan, Italy, September 15-18 2009. Google Scholar
  13. 13.
    Portugal, D., Rocha, R.: A Survey on Multi-Robot Patrolling Algorithms. In: Camarinha-Matos, L.M. (ed.) DoCEIS 2011. IFIP AICT, vol. 349, pp. 139–146. Springer, Heidelberg (2011)Google Scholar
  14. 14.
    Scheffé, H.: The Analysis of Variance. John Wiley & Sons, New York (1959)MATHGoogle Scholar
  15. 15.
    Krieger, M., Billeter, J., Keller, L.: Ant-like task allocation and recruitment in cooperative robots. Nature 406, 992–995 (2000)CrossRefGoogle Scholar
  16. 16.
    Guimarães, R.C., Sarsfield Cabral, J.A.: Estatística. McGraw-Hill (1997)Google Scholar
  17. 17.
    Quigley, M., Gerkey, B., Conley, K., Faust, J., Foote, T., Leibs, J., Berger, E., Wheeler, R., Ng, A.: ROS: an open-source Robot Operating System. In: Proceedings of the 2009 IEEE International Conference on Robotics and Automation (ICRA 2009), Workshop on Open Source Software, Kobe, Japan, May 12-17 (2009)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2012

Authors and Affiliations

  • David Portugal
    • 1
  • Rui P. Rocha
    • 1
  1. 1.Institute of Systems and Robotics, Department of Electrical and Computer EngineeringUniversity of CoimbraCoimbraPortugal

Personalised recommendations