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群体层面实现自主性的关键能力。关于群体机器人集体决策的大量文献集中在从有限选项中进行离散选择。本文中，我们赋予分散式机器人系统一项任务：探索无边界的连续环境，就可测量环境特征的平均值达成共识，并在测量到该值的区域（例如等高线）聚集。该任务的独特之处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，网络的平均节点度数影响收敛时间，而当前商定的平均值影响群体的聚集位置，进而影响网络结构以及精度误差。我们提出了一种控制算法，并在不同环境中的真实机器人群体实验中对其进行了研究。我们表明，该方法有效，并且比对照实验实现了更高的精度。我们预计该技术可应用于例如利用水面机器人进行污染控制等场景。