  Collective decision-making is an essential capability of large-scale
multi-robot systems to establish autonomy on the swarm level. A large portion
of literature on collective decision-making in swarm robotics focuses on
discrete decisions selecting from a limited number of options. Here we assign a
decentralized robot system with the task of exploring an unbounded environment,
finding consensus on the mean of a measurable environmental feature, and
aggregating at areas where that value is measured (e.g., a contour line). A
unique quality of this task is a causal loop between the robots' dynamic
network topology and their decision-making. For example, the network's mean
node degree influences time to convergence while the currently agreed-on mean
value influences the swarm's aggregation location, hence, also the network
structure as well as the precision error. We propose a control algorithm and
study it in real-world robot swarm experiments in different environments. We
show that our approach is effective and achieves higher precision than a
control experiment. We anticipate applications, for example, in containing
pollution with surface vehicles.
