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Multiple-Robot Systems (MRS) that is decentrally organized has many benefits over centralized systems. Decentralized systems are less affected by computational and communicative bottlenecks, and they are more robust to the loss of individual member robots. System-level cognitive operations, though, are much more difficult to implement in decentralized systems. One example is the best-of-N decision-making problem, in which a team attempts to unanimously select a single alternative from a list that maximizes a given metric. This is a valuable operation, since many system-level operations can be expressed in this form. Optimal best-of-N decision-making, however, is intractable in large decentralized systems. The contribution of this work is a biologically inspired algorithm that enables a decentralized MRS composed of very simple robots to make good, unanimous decisions.
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