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Performance of Communicating Cognitive Agents in Cooperative Robot Teams

  • Changyun WeiEmail author
  • Koen V. Hindriks
  • Catholijn M. Jonker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8946)

Abstract

In this work, we investigate the effectiveness of communication strategies in the coordination of cooperative robot teams. Robots are required to perform search and retrieval tasks, in which they need to search targets of interest in the environment and deliver them back to a home base. To study communication strategies in robot teams, we first discuss a case without communication, which is considered as the baseline, and also analyse various kinds of coordination strategies for robots to explore and deliver the targets in such a setting. We proceed to analyse three communication cases, where the robots can exchange their beliefs and/or goals with one another. Using communicated information, the robots can develop more complicated protocols to coordinate their activities. We use the Blocks World for Teams (BW4T) as the simulator to carry out experiments, and robots in the BW4T are controlled by cognitive agents. The team goal of the robots is to search and retrieve a sequence of colored blocks from the environment. In terms of cooperative teamwork, we have studied two main variations: a variant where all blocks to be retrieved have the same color (no ordering constraints on the team goal) and a variant where blocks of various colors need to be retrieved in a particular order (with ordering constraints). The experimental results show that communication will be particularly helpful to enhance the team performance for the second variant, and exchanging more information does not always yield a better team performance.

Keywords

Communication Multi-robot coordination Foraging 

References

  1. 1.
    Cao, Y.U., Fukunaga, A.S., Kahng, A.B.: Cooperative mobile robotics: antecedents and directions. Auton. Robot. 4, 1–23 (1997)CrossRefGoogle Scholar
  2. 2.
    Campo, A., Dorigo, M.: Efficient multi-foraging in swarm robotics. In: Almeida e Costa, F., Rocha, L.M., Costa, E., Harvey, I., Coutinho, A. (eds.) ECAL 2007. LNCS (LNAI), vol. 4648, pp. 696–705. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  3. 3.
    Krannich, S., Maehle, E.: Analysis of spatially limited local communication for multi-robot foraging. In: Kim, J.-H., Ge, S.S., Vadakkepat, P., Jesse, N., Al Manum, A., Puthusserypady K, S., Rückert, U., Sitte, J., Witkowski, U., Nakatsu, R., Braunl, T., Baltes, J., Anderson, J., Wong, C.-C., Verner, I., Ahlgren, D. (eds.) Progress in Robotics. CCIS, vol. 44, pp. 322–331. Springer, Heidelberg (2009) CrossRefGoogle Scholar
  4. 4.
    Farinelli, A., Iocchi, L., Nardi, D.: Multirobot systems: a classification focused on coordination. IEEE Trans. Syst. Man Cybern. 34, 2015–2028 (2004)CrossRefGoogle Scholar
  5. 5.
    Balch, T., Arkin, R.C.: Communication in reactive multiagent robotic systems. Auton. Robot. 1, 27–52 (1994)CrossRefGoogle Scholar
  6. 6.
    Ulam, P., Balch, T.: Using optimal foraging models to evaluate learned robotic foraging behavior. Adapt. Behav. 12, 213–222 (2004)CrossRefGoogle Scholar
  7. 7.
    Mohan, Y., Ponnambalam, S.: An extensive review of research in swarm robotics. In: World Congress on Nature & Biologically Inspired Computing, pp. 140–145. IEEE (2009)Google Scholar
  8. 8.
    Parker, L.E.: Distributed intelligence: overview of the field and its application in multi-robot systems. J. Phys. Agents 2, 5–14 (2008)Google Scholar
  9. 9.
    Cannon-Bowers, J., Salas, E., Converse, S.: Shared mental models in expert team decision making. In: Castellan, N.J. (ed.) Individual and Group Decision Making, pp. 221–245. Lawrence Erlbaum Associates, Hillsdale (1993)Google Scholar
  10. 10.
    Jonker, C. M., van de Riemsdijk, B., van de Kieft, I. C., Gini, M.: Towards measuring sharedness of team mental models by compositional means. In: Proceedings of 25th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE), pp. 242–251 (2012)Google Scholar
  11. 11.
    Rosenfeld, A., Kaminka, G.A., Kraus, S., Shehory, O.: A study of mechanisms for improving robotic group performance. Artif. Intell. 172, 633–655 (2008)CrossRefzbMATHGoogle Scholar
  12. 12.
    Rybski, P.E., Larson, A., Veeraraghavan, H., Anderson, M., Gini, M.: Performance evaluation of a multi-robot search & retrieval system: experiences with mindart. J. Intell. Robot. Syst. 52, 363–387 (2008)CrossRefGoogle Scholar
  13. 13.
    Davids, A.: Urban search and rescue robots: from tragedy to technology. IEEE Intell. Syst. 17, 81–83 (2002)Google Scholar
  14. 14.
    Yuh, J.: Design and control of autonomous underwater robots: a survey. Auton. Robot. 8, 7–24 (2000)CrossRefGoogle Scholar
  15. 15.
    Hindriks, K.: The goal agent programming language (2013). http://ii.tudelft.nl/trac/goal

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Changyun Wei
    • 1
    Email author
  • Koen V. Hindriks
    • 1
  • Catholijn M. Jonker
    • 1
  1. 1.Interactive Intelligence Group, EEMCSDelft University of TechnologyDelftThe Netherlands

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