An efficient coordination architecture for autonomous robot teams

Abstract

Most physically implemented multi-robot controllers are based on extensions of behavior-based systems. While efficient, such techniques suffer from a paucity of representational power. Symbolic systems, on the other hand, have more sophisticated representations but are computationally complex and have model coherency issues. We describe HIVEMind, a tagged behavior-based architecture for small teams of cooperative robots. In HIVEMind, robots share inferences and sensory data by treating other team members as virtual sensors connected by wireless links. A representation based on bit-vectors allows team members to share intentional, attentional, and sensory information using relatively low-bandwidth connections. We describe an application of the architecture to the problem of systematic spatial search.