Journal of Intelligent and Robotic Systems

, Volume 52, Issue 3–4, pp 363–387 | Cite as

Performance Evaluation of a Multi-Robot Search & Retrieval System: Experiences with MinDART

  • Paul E. Rybski
  • Amy Larson
  • Harini Veeraraghavan
  • Monica Anderson
  • Maria Gini


Swarm techniques, where many simple robots are used instead of complex ones for performing a task, promise to reduce the cost of developing robot teams for many application domains. The challenge lies in selecting an appropriate control strategy for the individual units. This work explores the effect of control strategies of varying complexity and environmental factors on the performance of a team of robots at a foraging task when using physical robots (the Minnesota Distributed Autonomous Robotic Team). Specifically we study the effect of localization and of simple indirect communication techniques on task completion time using two sets of foraging experiments. We also present results for task performance with varying team sizes and target distributions. As indicated by the results, control strategies with increasing complexity reduce the variance in the performance, but do not always reduce the time to complete the task.


Multi-robot systems Search and retrieval Performance evaluation MinDART 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Anderson, C.: Self-organization in relation to several similar concepts: are the boundaries to self-organization indistinct? Biol. Bull. 202, 247–255 (2002) (June)CrossRefGoogle Scholar
  2. 2.
    Arkin, R.C.: Motor schema-based robot navigation. Int. J. Rob. Res. 8(4), 92–112 (1989) (August)CrossRefGoogle Scholar
  3. 3.
    Arkin, R.C.: Cooperation without communication: multi-agent schema based robot navigation. J. Robot. Syst. 9(3), 351–364 (1992) (April)CrossRefGoogle Scholar
  4. 4.
    Arkin, R.C., Bekey, G.A. (eds.): Robot Colonies. Kluwer Academic Publishers, Boston, MA (1997)MATHGoogle Scholar
  5. 5.
    Balch, T.: Hierarchical social entropy: an information theoretic measure of robot group diversity. Auton. Robots 8, 209–237 (2000)CrossRefGoogle Scholar
  6. 6.
    Balch, T., Arkin, R.C.: Communication in reactive multiagent robotic systems. Auton. Robots 1(1), 27–52 (1995)CrossRefGoogle Scholar
  7. 7.
    Bayindir, L., Sahin, E.: A review of studies in swarm robotics. Turk. J. Elec. Eng. 15(2), 115–147 (2007)Google Scholar
  8. 8.
    Beckers, R., Holland, O.E., Deneubourg, J.L.: From local actions to global tasks: stigmergy in collective robotics. In: Brooks, R., Maes, P. (eds.) Artificial Life IV, pp. 181–189. MIT Press, Cambridge, MA (1994)Google Scholar
  9. 9.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: from Natural to Artificial Systems. Oxford University Press, Oxford (1999)MATHGoogle Scholar
  10. 10.
    Brooks, R.A.: A robust layered control system for a mobile robot. IEEE J. Robot. Autom. RA-2(1), 14–23 (1986) (March)Google Scholar
  11. 11.
    Drogoul, A., Ferber, J.: From tom thumb to the dockers: some experiments with foraging robots. In: From Animals to Animats. Proc. Int’l Conf. on Simulation of Adaptive Behavior, pp. 451–459. MIT Press, Cambridge, MA (1992)Google Scholar
  12. 12.
    Dudek, G., Jenkin, M., Milios, E., Wilkes, D.: A taxonomy for multi-agent robotics. Auton. Robots 3, 375–397 (1996)Google Scholar
  13. 13.
    Easton, K., Martinoli, A.: Efficiency and optimization of explicit and implicit communication schemes in collaborative robotics experiments. In: Proc. IEEE/RSJ Int’l Conf. on Intelligent Robots and Systems, pp. 2795–2800. IEEE, Lausanne (2002)CrossRefGoogle Scholar
  14. 14.
    Fukuda, T., Funato, D., Sekiyam, K., Arai, F.: Evaluation on flexibility of swarm intelligent system. In: Proc. of the IEEE Int’l Conf. on Robotics and Automation, pp. 3210–3215. IEEE, Piscataway (1998)Google Scholar
  15. 15.
    Goldberg, D., Matarić, M.J.: Design and evaluation of robust behavior-based controllers. In: Balch, T., Parker, L.E. (eds.) Robot Teams: From Diversity to Polymorphism. A K Peters Ltd, Natick, MA (2002) (April)Google Scholar
  16. 16.
    Grassé, P.P.: La reconstruction du nid et les coordinations interindividuelles chez bellicositermes natalensis et cubitermes sp. la theorie de la stigmergie: essai d’interpretation des termites constructeurs. Ins. Soc. 6, 41–83 (1959)CrossRefGoogle Scholar
  17. 17.
    Hamann, H., Worn, H.: An analytical and spatial model of foraging in a swarm of robots. In: Sahin, E., Spears, W.M., Winfield, A.F.T. (eds.) Swarm Robotics. Lecture Notes in Computer Science, vol. 4433, pp. 43–55. Springer, New York (2006)Google Scholar
  18. 18.
    Hayes, A.T.: How many robots? group size and efficiency in collective search tasks. In: Proc. Int’l Symp. on Distributed Autonomous Robotic Systems, pp. 289–298. Springer, Berlin (2002) (June)Google Scholar
  19. 19.
    Holland, O., Melhuish, C.: Stigmergy, self-organisation, and sorting in collective robotics. Artif. Life 5, 173–202 (2000)CrossRefGoogle Scholar
  20. 20.
    Hölldobler, B., Wilson, E.O.: The multiple recruitment systems of the african weaver ant oecophylla longinoda (latreille). Behav. Ecol. Sociobiol. 3, 19–60 (1978)CrossRefGoogle Scholar
  21. 21.
    Hougen, D.F., Rybski, P.E., Gini, M.: Repeatability of real world training experiments: a case study. Auton. Robots 6(2), 281–292 (1998)Google Scholar
  22. 22.
    Labella, T.H.: Prey retrieval by a swarm of robots. Technical report, TR/IRIDIA/2003-16, IRIDIA, Université Libre de Bruxelles, DEA thesis (2003) (May)Google Scholar
  23. 23.
    Lambrinos, D., Möller, R., Labhart, T., Pfeifer, R., Wehner, R.: A mobile robot employing insect strategies for navigation. Robot. Auton. Syst. 30, 39–64 (2000)CrossRefGoogle Scholar
  24. 24.
    Martin, F.G.: The Handy Board Technical Reference. MIT Media Laboratory, Cambridge, MA (1998)Google Scholar
  25. 25.
    McLurkin, J., Yamins, D.: Dynamic task assignment in robot swarms. In: Robotics: Science and Systems Conference. Cambridge, MA (2005) (June)Google Scholar
  26. 26.
    Nguyen, T.N., O’Donnell, C., Nguyen, T.B.: Multiple autonomous robots for uxo clearance, the basic uxo gathering system (bugs) project. In: Schultz, A.C., Parker, L.E. (eds.) Multi-Robot Systems: from Swarms to Intelligent Automata, pp. 53–61. Kluwer Academic, Boston, MA (2002)Google Scholar
  27. 27.
    Rowe, A., Rosenberg, C., Nourbakhsh, I.: A low cost embedded color vision system. In: Proc. IEEE/RSJ Int’l Conf. on Intelligent Robots and Systems. IEEE, Lausanne (2002)Google Scholar
  28. 28.
    Rybski, P., Larson, A., Veeraraghavan, H., LaPoint, M., Gini, M.: Communication strategies in multi-robot search and retrieval: experiences with MinDART. In: Proc. Int’l Symp. on Distributed Autonomous Robotic Systems, pp. 317–326, Toulouse, 23–25 June 2004Google Scholar
  29. 29.
    Rybski, P.E., Stoeter, S.A., Gini, M., Hougen, D.F., Papanikolopoulos, N.: Performance of a distributed robotic system using shared communications channels. IEEE Trans. Robot. Autom. 22(5), 713–727 (2002) (October)CrossRefGoogle Scholar
  30. 30.
    Rybski, P.E., Larson, A., Schoolcraft, A., Osentoski, S., Gini, M.: Evaluation of control strategies for multi-robot search and retrieval. In: Proc. Int’l Conf. on Intelligent Autonomous Systems, pp. 281–288. Marina Del Rey, CA (2002) (March)Google Scholar
  31. 31.
    Seeley, T.D.: The honeybee colony as a superorganism. Am. Sci. 77, 546–553 (1989)Google Scholar
  32. 32.
    Seth, A.K.: Unorthodox optimal foraging theory. In: From Animals to Animats. Proc. Int’l Conf. on Simulation of Adaptive Behavior, pp. 478–481. MIT Press, Cambridge, MA (2000)Google Scholar
  33. 33.
    Sharkey, A.J.C.: Swarm robotics and minimalism. Connect. Sci. 19(3), 245–260 (2007) (September)CrossRefGoogle Scholar
  34. 34.
    Sugawara, K., Sano, M.: Cooperative acceleration of task performance: foraging behavior of interacting multi-robots system. Physica D100, 343–354 (1997)Google Scholar
  35. 35.
    Sugawara, K., Watanabe, T.: Swarming robots—foraging behavior of simple multi-robot systems. In: Proc. IEEE/RSJ Int’l Conf. on Intelligent Robots and Systems. IEEE, Lausanne (2002)Google Scholar
  36. 36.
    Sugawara, K., Yoshihara, I., Abe, K.: A scaling law between the number of multirobots and their task performance. In: Artificial Life and Robotics, vol. 3, pp. 122–126. Springer, Tokyo (1999)Google Scholar
  37. 37.
    Weber, K., Venkatesh, S., Srinivasan, M.: Insect-inspired robotic homing. Adapt. Behav. 7(1), 65–97 (1999)CrossRefGoogle Scholar
  38. 38.
    Werfel, J., Nagpal, R.: Extended stigmergy in collective construction. IEEE Intel. Syst. 21(2), 20–28 (2006)CrossRefGoogle Scholar
  39. 39.
    Winfield, A.F.T., Nembrini, J.: Safety in numbers: fault-tolerance in robot swarms. Int. J. Model. Identif. Cont. 1(1), 30–37 (2006)CrossRefGoogle Scholar
  40. 40.
    Wright, A., Sargent, R., Witty, C.: Interactive C User’s Guide. Newton Research Labs, Cambridge, MA (1996)Google Scholar
  41. 41.
    Zhang, D., Xie, G., Yu, J., Wang, L.: Adaptive task assignment for multiple mobile robots via swarm intelligence approach. Robot. Auton. Syst. 55(7), 572–588 (2007)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  • Paul E. Rybski
    • 1
  • Amy Larson
    • 1
  • Harini Veeraraghavan
    • 1
  • Monica Anderson
    • 1
  • Maria Gini
    • 1
  1. 1.Department of Computer Science and EngineeringUniversity of MinnesotaMinneapolisUSA

Personalised recommendations