Abstract
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.
Abstract (translated)
集体决策是大规模多机器人系统建立群体自治的关键能力。在群体机器人学中,大量的文献都集中在从有限选项中选择离散决策的问题。在这里,我们委托一个分散化的机器人系统去探索一个不受限制的环境,找到可测量环境特征的平均数,并在这些值被测量的地方进行聚合(例如,地形线)。这个任务的独特的特征是机器人的动态网络拓扑与其决策之间的因果关系循环。例如,网络的平均节点度数会影响收敛的时间,而当前商定的平均值会影响群体聚合的位置,因此也影响网络结构和精度误差。我们提出了一个控制算法,并在不同的环境中进行了实际机器人群体实验。我们表明,我们的方法和控制实验相比更有效,精度更高。我们预计这种方法可以应用于例如,与陆上车辆排放有关的污染物控制。
URL
https://arxiv.org/abs/2302.13629
