How Much Worth Is Coordination of Mobile Robots for Exploration in Search and Rescue?

  • Francesco Amigoni
  • Nicola Basilico
  • Alberto Quattrini Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7500)

Abstract

Exploration of unknown environments is an enabling task for several applications, including map building and search and rescue. It is widely recognized that several benefits can be derived from deploying multiple mobile robots in exploration, including increased robustness and efficiency. Two main issues of multirobot exploration are the exploration strategy employed to select the most convenient observation locations the robots should reach in a partially known environment and the coordination method employed to manage the interferences between the actions performed by robots. From the literature, it is difficult to assess the relative effects of these two issues on the system performance. In this paper, we contribute to filling this gap by studying a search and rescue setting in which different coordination methods and exploration strategies are implemented and their contributions to an efficient exploration of indoor environments are comparatively evaluated. Although preliminary, our experimental data lead to the following results: the role of exploration strategies dominates that of coordination methods in determining the performance of an exploring multirobot system in a highly structured indoor environment, while the situation is reversed in a less structured indoor environment.

Keywords

search and rescue exploration coordination multirobot 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Burgard, W., Moors, M., Schneider, F.: Coordinated multi-robot exploration. IEEE T. Robot. 21(3), 376–378 (2005)CrossRefGoogle Scholar
  2. 2.
    Burgard, W., Fox, D., Moors, M., Simmons, R., Thrun, S.: Collaborative multi-robot exploration. In: Proc. ICRA, pp. 476–481 (2000)Google Scholar
  3. 3.
    Sariel, S., Balch, T.: Real time auction based allocation of tasks for multi-robot exploration problem in dynamic environments. In: Proc. AAAI Workshop on Integrating Planning and Scheduling, pp. 27–33 (2005)Google Scholar
  4. 4.
    Amigoni, F.: Experimental evaluation of some exploration strategies for mobile robots. In: Proc. ICRA, pp. 2818–2823 (2008)Google Scholar
  5. 5.
    Gerkey, B., Mataric, M.: A formal analysis and taxonomy of task allocation in multi-robot systems. Int. J. Robot. Res. 23, 939–954 (2004)CrossRefGoogle Scholar
  6. 6.
    Carpin, S., Lewis, M., Wang, J., Balakirsky, S., Scrapper, C.: USARSim: A robot simulator for research and education. In: Proc. ICRA, pp. 1400–1405 (2007)Google Scholar
  7. 7.
    Visser, A., Slamet, B.: Including communication success in the estimation of information gain for multi-robot exploration. In: Proc. WiOPT, pp. 680–687 (2008)Google Scholar
  8. 8.
    Lopez-Sanchez, M., Esteva, F., Lopez de Mantaras, R., Sierra, C., Amat, J.: Map generation by cooperative low-cost robots in structured unknown environments. Auton. Robot. 5, 53–61 (1998)CrossRefGoogle Scholar
  9. 9.
    Ko, J., Stewart, B., Fox, D., Konolige, K., Limketkai, B.: A practical, decision-theoretic approach to multi-robot mapping and exploration. In: Proc. IROS, pp. 3232–3238 (2003)Google Scholar
  10. 10.
    Marjovi, A., Nunes, J., Marques, L., de Almeida, A.: Multi-robot exploration and fire searching. In: Proc. IROS, pp. 1929–1934 (2009)Google Scholar
  11. 11.
    Tovey, C., Koenig, S.: Improved analysis of greedy mapping. In: Proc. IROS, pp. 3251–3257 (2003)Google Scholar
  12. 12.
    Yamauchi, B.: Frontier-based exploration using multiple robots. In: Proc. Int’l Conf. Autonomous Agents, pp. 47–53 (1998)Google Scholar
  13. 13.
    Stachniss, C., Burgard, W.: Exploring unknown environments with mobile robots using coverage maps. In: Proc. IJCAI, pp. 1127–1134 (2003)Google Scholar
  14. 14.
    Gonzáles-Baños, H., Latombe, J.C.: Navigation strategies for exploring indoor environments. Int. J. Robot. Res. 21(10-11), 829–848 (2002)CrossRefGoogle Scholar
  15. 15.
    Amigoni, F., Caglioti, V.: An information-based exploration strategy for environment mapping with mobile robots. Robot. Auton. Syst. 5(58), 684–699 (2010)CrossRefGoogle Scholar
  16. 16.
    Tovar, B., Munoz, L., Murrieta-Cid, R., Alencastre, M., Monroy, R., Hutchinson, S.: Planning exploration strategies for simultaneous localization and mapping. Robot. Auton. Syst. 54(4), 314–331 (2006)CrossRefGoogle Scholar
  17. 17.
    Amigoni, F., Gallo, A.: A multi-objective exploration strategy for mobile robots. In: Proc. ICRA, pp. 3861–3866 (2005)Google Scholar
  18. 18.
    Basilico, N., Amigoni, F.: Exploration strategies based on multi-criteria decision making for searching environments in rescue operations. Auton. Robot. 31(4), 401–417 (2011)CrossRefGoogle Scholar
  19. 19.
    Calisi, D., Farinelli, A., Iocchi, L., Nardi, D.: Multi-objective exploration and search for autonomous rescue robots. J. Field. Robot. 24(8-9), 763–777 (2007)CrossRefGoogle Scholar
  20. 20.
    Fox, D., Ko, J., Konolige, K., Limketkai, B., Schulz, D., Stewart, B.: Distributed multirobot exploration and mapping. Proc. IEEE 94(7), 1325–1339 (2006)CrossRefMATHGoogle Scholar
  21. 21.
    Simmons, R., Apfelbaum, D., Burgard, W., Fox, D., Moors, M., Thrun, S., Younes, H.: Coordination for multi-robot exploration and mapping. In: Proc. AAAI, pp. 852–858 (2000)Google Scholar
  22. 22.
    Zlot, R., Stentz, A., Dias, M.B., Thayer, S.: Multi-robot exploration controlled by a market economy. In: Proc. ICRA, pp. 3016–3023 (2002)Google Scholar
  23. 23.
    Hawley, J., Butler, Z.: Hierarchical distributed task allocation for multi-robot exploration. In: Proc. DARS, pp. 445–458 (2010)Google Scholar
  24. 24.
    Visser, A., de Buy Wenniger, G., Nijhuis, H., Alnajar, F., Huijten, B., van der Velden, M., Josemans, W., Terwijn, B., Sobolewski, R., Flynn, H., de Hoog, J.: Amsterdam Oxford joint rescue forces - Team description paper - RoboCup 2009. In: Proc. RoboCup (2009)Google Scholar
  25. 25.
    Pestman, W.: Mathematical Statistics: an Introduction. de Gruyter (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Francesco Amigoni
    • 1
  • Nicola Basilico
    • 2
  • Alberto Quattrini Li
    • 1
  1. 1.Politecnico di MilanoMilanoItaly
  2. 2.University of CaliforniaMercedUSA

Personalised recommendations