Fast Redistribution of a Swarm of Heterogeneous Robots

We present a method that distributes a swarm of heterogeneous robots among a set of tasks that require specialized capabilities in order to be completed. We model the system of heterogeneous robots as a community of species, where each species (robot type) is defined by the traits (capabilities) that it owns. Our method is based on a continuous abstraction of the swarm at a macroscopic level, as we model robots switching between tasks. We formulate an optimization problem that produces an optimal set of transition rates for each species, so that the desired trait distribution among the tasks is reached as quickly as possible. Our solution is based on an analytical gradient, and is computationally efficient, even for large choices of traits and species. Finally, we show that our method is capable of producing fast convergence times when compared to state-of-the-art methods.