Advertisement

Value of Incomplete Information in Mobile Target Allocation

  • Marin Lujak
  • Stefano Giordani
  • Sascha Ossowski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6973)

Abstract

In this paper, we consider a decentralized approach to the multi-agent target allocation problem where agents are partitioned in two groups and every member of each group is a possible target for the members of the opposite group. Each agent has a limited communication range (radius) and individual preferences for the target allocation based on its individual local utility function. Furthermore, all agents are mobile and the allocation is achieved through a proposed dynamic iterative auction algorithm. Every agent in each step finds its best target based on the auction algorithm and the exchange of information with connected agents and moves towards it without any insight in the decision-making processes of other agents in the system. In the case of connected communication graph among all agents, the algorithm results in an optimal allocation solution. We explore the deterioration of the allocation solution in respect to the decrease of the quantity of the information exchanged among agents and agents’ varying communication range when the latter is not sufficient to maintain connected communication graph.

Keywords

Multiagent System Mobile Agent Communication Range Communication Graph Random Geometric Graph 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Beard, R.W., McLain, T.W.: Multiple UAV cooperative search under collision avoidance and limited range communication constraints. In: Proc. of Conf. on Decision and Control, vol. 1, pp. 25–30. IEEE, Los Alamitos (2003)Google Scholar
  2. 2.
    Bertsekas, D.P.: Auction algorithms for network flow problems: A tutorial introduction. Comput. Opt. and Applications 1(1), 7–66 (1992)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Dias, M.B., Zlot, R., Kalra, N., Stentz, A.: Market-based multirobot coordination: A survey and analysis. Proc. of IEEE, Spec. iss. on multirob. coord. 94(7), 1257–1270 (2006)Google Scholar
  4. 4.
    Díaz, J., Mitsche, D., et al.: On the connectivity of dynamic random geometric graphs. In: Proc. of 19th ACM-SIAM Symposium on Discrete Algorithms, pp. 601–610 (2008)Google Scholar
  5. 5.
    Elango, M., Nachiappan, S., Tiwari, M.K.: Balancing task allocation in multi-robot systems using k means clustering and auction based mechanisms. Expert Systems With Applications 38(6), 6486–6491 (2010)CrossRefGoogle Scholar
  6. 6.
    Gerkey, B.P., Matarić, M.J.: A formal analysis and taxonomy of task allocation in multi-robot systems. The Intern. Journ. of Rob. Research 23(9), 939–954 (2004)CrossRefGoogle Scholar
  7. 7.
    Gerkey, B.P., Matarić, M.J.: Multi-robot task allocation: Analyzing the complexity and optimality of key architectures. In: Proc. of ICRA 2003, vol. 3, pp. 3862–3868 (2003)Google Scholar
  8. 8.
    Gerkey, B.P., Matarić, M.J.: Sold!: Auction methods for multi-robot control. Proc. IEEE Trans. on Robotics and Automat. 18(5), 758–768 (2002)CrossRefGoogle Scholar
  9. 9.
    Gil, A.E., Passino, K.M., Ganapathy, S., Sparks, A.: Cooperative scheduling of tasks for networked uninhabited autonomous vehicles. In: Proc. of 42nd IEEE Conf. on Decision and Control, pp. 522–527 (2003)Google Scholar
  10. 10.
    Hoeing, M., Dasgupta, P., Petrov, P., O’Hara, S.: Auction-based multi-robot task allocation in comstar. In: Proc. of the 6th Inter. Joint Conf. on Auton. Agents and Multiagent Systems, pp. 1–8 (2007)Google Scholar
  11. 11.
    Lerman, K., Jones, C., Galstyan, A., Matarić, M.J.: Analysis of dynamic task allocation in multi-robot systems. The Intern. Journal of Robotics Research 25(3), 225–253 (2006)CrossRefGoogle Scholar
  12. 12.
    Lujak, M., Giordani, S.: On the communication range in auction-based multi-agent target assignment. In: Bettstetter, C., Gershenson, C. (eds.) IWSOS 2011. LNCS, vol. 6557, pp. 32–43. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  13. 13.
    Michael, N., Zavlanos, M., Kumar, V., Pappas, G.: Maintaining connectivity in mobile robot networks. Experim. Robotics, 117–126 (2009)Google Scholar
  14. 14.
    Nanjanath, M., Gini, M.: Repeated auctions for robust task execution by a robot team. Robotics and Autonomous Sys. 58(7), 900–909 (2010)CrossRefGoogle Scholar
  15. 15.
    Penrose, M.: Random geometric graphs. Oxford Univ. Press, USA (2003)CrossRefzbMATHGoogle Scholar
  16. 16.
    Santi, P.: The critical transmitting range for connectivity in mobile ad hoc networks. IEEE Trans. on Mob. Comp. 4(3), 310–317 (2005)CrossRefGoogle Scholar
  17. 17.
    Santi, P., Blough, D.M.: An evaluation of connectivity in mobile wireless ad hoc networks. In: Proc. of Int. Conf. on Depend. Sys. and Netw., pp. 89–98 (2002)Google Scholar
  18. 18.
    Santi, P., Blough, D.M., Vainstein, F.: A probabilistic analysis for the range assignment problem in ad-hoc networks. In: Proc. of ACM Mobihoc, pp. 212–220 (2001)Google Scholar
  19. 19.
    Savkin, A.V.: The problem of coordination and consensus achievement in groups of autonomous mobile robots with limited communication. Nonlinear Analysis 65(5), 1094–1102 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Zavlanos, M.M., Spesivtsev, L., Pappas, G.J.: A distributed auction algorithm for the assignment problem. In: Proc. of 47th IEEE Conf. on Dec. and Contr., pp. 1212–1217 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Marin Lujak
    • 1
  • Stefano Giordani
    • 2
  • Sascha Ossowski
    • 1
  1. 1.University Rey Juan CarlosMadridSpain
  2. 2.Dip. Ingegneria dell’ImpresaUniversity of Rome “Tor Vergata”Italy

Personalised recommendations