Self-organized Clustering of Square Objects by Multiple Robots

Object clustering is a widely studied task in which self-organized robots form piles from dispersed objects. Although central clusters are usually desired, workspace boundaries can cause perimeter cluster formation to dominate. This research demonstrates successful clustering of square boxes --an especially challenging instance since flat edges exacerbate adhesion to boundaries-- using simpler robots than previous published research. Our solution consists of two novel behaviors, Twisting and Digging, which exploit the objects' geometry to pry boxes free from boundaries. We empirically explored the significance of different divisions of labor by measuring the spatial distribution of robots and the system performance. Data from over 40 hours of physical robot experiments show that different divisions of labor have distinct features, e.g., one is reliable while another is especially efficient.