Improving the performance of self-organized robotic clustering: Modeling and planning sequential changes to the division of labor

Robotic clustering involves gathering spatially distributed objects into a single pile. It is a canonical task for self-organized multi-robot systems: several authors have proposed and demonstrated algorithms for performing the task. In this paper, we consider a setting in which heterogeneous strategies outperform homogeneous ones and changing the division of labor can improve performance. By modeling the clustering dynamics with a Markov chain model, we are able to predict performance of the task by different divisions of labor. We propose and demonstrate a method that is able to select an open-loop sequence of changes to the division of labor, based on this stochastic model, that increases performance. We validate our proposed method on physical robot experiments.