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 its pose and 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：人机集群交互通过低维度的集群形态编码实现，该编码规模与机器人数量（可能很大）无关。本文提出利用图像矩对机器人的二维形态进行编码。每个机器人可获知自身位姿及目标形态矩，并在运动控制过程中同步估计整个集群的当前矩，从而优化集群矩的达成效果。该估计器采用分布式优化架构，无需集中处理，且具备自修复能力——对初始误差、数据包丢失以及集群中机器人的增删均具有鲁棒性。我们在50台机器人组成的集群实验（数据包丢失率近50%）中证实，分布式图像矩估计与控制系统能有效达成目标集群形态。