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 dis-tributed and iterative decision-making strategy. Using this strategy, robots explore the available options, determine the options ’ qualities, decide au-tonomously which option to take, and communicate their decision to neighboring robots. We study the effectiveness and robustness of the pro-posed 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 num-ber 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. 1