Adaptive division of Labor in large-scale minimalist multi-robot systems

A Large-Scale Minimalist Multi-Robot System (LMMS) is one composed of a group of robots each with limited capabilities in terms of sensing, computation, and communication. Such systems have received increased attention due to their empirically demonstrated performance and beneficial characteristics, such as their robustness to environmental perturbations and individual robot failure and their scalability to large numbers of robots. However, little work has been done in investigating ways to endow such a LMMS with the capability to achieve a desired division of labor over a set of dynamically evolving concurrent tasks, important in many task-achieving LMMS. Such a capability can help to increase the efficiency and robustness of overall task performance as well as open new domains in which LMMS can be seen as a viable alternative to more complex control solutions. In this paper we present a method for achieving a desired division of labor in a LMMS, experimentally validate it in a realistic simulation, and demonstrate its potential to scale to large numbers of robots and its ability to adapt to environmental perturbations.