Human-swarm interaction is facilitated by a low-dimensional encoding of the swarm formation, independent of the (possibly large) number of robots. We propose using image moments to encode two-dimensional formations of robots. Each robot knows the desired formation moments, and simultaneously estimates the current moments of the entire swarm while controlling its motion to better achieve the desired group moments. The estimator is a distributed optimization, requiring no centralized processing, and self-healing, meaning that the process is robust to initialization errors, packet drops, and robots being added to or removed from the swarm. Our experimental results with a swarm of 50 robots, suffering nearly 50% packet loss, show that distributed estimation and control of image moments effectively achieves desired swarm formations.

翻译：人-集群交互通过低维的集群编队编码实现，且该编码与机器人数量（可能很大）无关。我们提出利用图像矩对二维机器人编队进行编码。每个机器人已知期望编队矩，在控制自身运动以更好地达成群体矩目标的同时，同步估计整个集群的当前矩。该估计器采用分布式优化方法，无需集中处理，并具备自愈能力——即对初始化错误、数据包丢失以及机器人的增减具有鲁棒性。我们使用由50个机器人构成的集群（面临近50%的数据包丢失率）进行的实验结果表明，分布式估计与图像矩控制能有效实现期望的集群编队。