Estimation of continuous environments by robot swarms: Correlated networks and decision-making
This addresses the challenge of continuous decision-making for robot swarms, with potential applications like pollution containment, but is incremental as it builds on existing swarm robotics methods.
The paper tackles the problem of enabling robot swarms to collectively estimate and aggregate around the mean of a continuous environmental feature, such as a contour line, in unbounded environments, and shows that their approach achieves higher precision than a control experiment.
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.