Efficient Decision-Making in a Self-Organizing Robot Swarm: On the Speed Versus Accuracy Trade-Off

Abstract

We study a self-organized collective decision-making strategy to solve the best-of-n decision problem in a swarm of robots. We define a distributed and iterative decision-making strategy. Using this strategy, robots explore the available options, determine the options' qualities, decide autonomously which option to take, and communicate their decision to neighboring robots. We study the effectiveness and robustness of the proposed strategy using a swarm of 100 Kilobots. We study the well-known speed versus accuracy trade-off analytically by developing a mean-field model. Compared to a previously published simpler method, our decision-making strategy shows a considerable speed-up but has lower accuracy. We analyze our decision-making strategy with particular focus on how the spatial density of robots impacts the dynamics of decisions. The number of neighboring robots is found to influence the speed and accuracy of the decision-making process. Larger neighborhoods speed up the decision but lower its accuracy. We observe that the parity of the neighborhood cardinality determines whether the system will over- or under-perform.