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.

翻译：集体决策是大规模多机器人系统在集群层面建立自主性的关键能力。现有集群机器人集体决策文献主要关注从有限选项中做出离散选择。本文赋予分散式机器人系统以下任务：探索无界环境、就可测量环境特征均值达成共识、并在测量到该值的区域（如等高线）聚集。该任务的一个独特性质在于：机器人动态网络拓扑与其决策之间存在因果循环。例如，网络平均节点度影响收敛时间，而当前达成一致的均值影响集群聚集位置，进而影响网络结构与精度误差。我们提出一种控制算法，并在不同环境的真实机器人集群实验中对其进行研究。实验表明，该方法有效且精度高于对照组。我们预期其应用于水面机器人污染围控等领域。