Coordinating mobile robot group behavior using a model of interaction dynamics

In this paper we show how various levels of coordinated behavior may be achieved in a group of mobile robots by using a model of the interaction dynamics between a robot and its environment. We present augmented Markov models (AMMs) as a tool for capturing such interaction dynamics on-line and in real-time, with little computational and storage overhead. We begin by describing the structure of AMMs and the algorithm for generating them, then verify the approach utilizing data from physical mobile robots performing elements of a foraging task. Finally, we demonstrate the application of the model for resolving group coordination issues arising from three sources: individual performance, group affiliation, and group performance. Corresponding respectively to these are the three experimental examples we present fault detection, group membership based on ability and experience, and dynamic leader selection.

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