  We seek methods to model, control, and analyze robot teams performing
environmental monitoring tasks. During environmental monitoring, the goal is to
have teams of robots collect various data throughout a fixed region for
extended periods of time. Standard bottom-up task assignment methods do not
scale as the number of robots and task locations increases and require
computationally expensive replanning. Alternatively, top-down methods have been
used to combat computational complexity, but most have been limited to the
analysis of methods which focus on transition times between tasks. In this
work, we study a class of nonlinear macroscopic models which we use to control
a time-varying distribution of robots performing different tasks throughout an
environment. Our proposed ensemble model and control maintains desired
time-varying populations of robots by leveraging naturally occurring
interactions between robots performing tasks. We validate our approach at
multiple fidelity levels including experimental results, suggesting the
effectiveness of our approach to perform environmental monitoring.
