Miscommunication between robots can improve group accuracy in best-of-n decision-making

Making fast and accurate consensus decisions through local communication and decentralised control in a swarm of simple robots can be a very challenging endeavour. In swarms of robots with limited capabilities, consensus decisions can be made using simple voting rules. In our study, the robots use rules based on the cross-inhibition model, which describes a voting mechanism observed in the house-hunting honeybee, that has been shown to efficiently allow consensus achievement in distributed robotic systems. The cross-inhibition mechanism has been shown to lead to a highly stable consensus, preventing the correction of possible group decision errors which can happen, for example, due to high noise in robots’ estimations. In this paper, we investigate the impact of miscommunication on the speed-accuracy trade-off in consensus decision-making in the context of a binary discrimination problem—i.e., choosing collectively the best of two options. We evaluate the accuracy of decision-making theoretically, using continuous and finite-size models, and experimentally in a collective perception scenario, using swarms of 100 simulated robots and 50 real Kilobots. Our study suggests that a certain level of miscommunication (or communication noise) among agents can increase the decision’s accuracy and, thus, can serve an important functional role in making collective decisions in robot swarms.