Non-communicative multi-robot coordination in dynamic environments

Jelle R. Kok, Matthijs T. J. Spaan, and Nikos Vlassis. Non-communicative multi-robot coordination in dynamic environments. Robotics and Autonomous Systems, 50(2-3):99–114, Elsevier Science, February 2005.

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Abstract

Within a group of cooperating agents the decision making of an individual agent depends on the actions of the other agents. In dynamic environments, these dependencies will change rapidly as a result of the continuously changing state. Via a context-specific decomposition of the problem into smaller subproblems, coordination graphs offer scalable solutions to the problem of multiagent decision making. In this work, we apply coordination graphs to a continuous (robotic) domain by assigning roles to the agents and then coordinating the different roles. Moreover, we demonstrate that, with some additional assumptions, an agent can predict the actions of the other agents, rendering communication superfluous. We have successfully implemented the proposed method into our UvA Trilearn simulated robot soccer team which won the RoboCup-2003 World Championship in Padova, Italy.

BibTeX Entry

@article{Kok05roas,
  author =       {Jelle R. Kok and Matthijs T. J. Spaan and Nikos
                  Vlassis},
  title =        {Non-communicative multi-robot coordination in
                  dynamic environments},
  journal =      {Robotics and Autonomous Systems},
  year =         2005,
  month =        feb,
  volume =       50,
  number =       {2-3},
  pages =        {99--114},
  publisher =    {Elsevier Science},
  abstract =     {Within a group of cooperating agents the decision
                  making of an individual agent depends on the actions
                  of the other agents. In dynamic environments, these
                  dependencies will change rapidly as a result of the
                  continuously changing state. Via a context-specific
                  decomposition of the problem into smaller
                  subproblems, coordination graphs offer scalable
                  solutions to the problem of multiagent decision
                  making. In this work, we apply coordination graphs
                  to a continuous (robotic) domain by assigning roles
                  to the agents and then coordinating the different
                  roles. Moreover, we demonstrate that, with some
                  additional assumptions, an agent can predict the
                  actions of the other agents, rendering communication
                  superfluous. We have successfully implemented the
                  proposed method into our UvA Trilearn simulated
                  robot soccer team which won the RoboCup-2003 World
                  Championship in Padova, Italy. }
}

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