  The problem of coverage control, i.e., of coordinating multiple agents to
optimally cover an area, arises in various applications. However, coverage
applications face two major challenges: (1) dealing with nonlinear dynamics
while respecting system and safety critical constraints, and (2) performing the
task in an initially unknown environment. We solve the coverage problem by
using a hierarchical framework, in which references are calculated at a central
server and passed to the agents' local model predictive control (MPC) tracking
schemes. Furthermore, to ensure that the environment is actively explored by
the agents a probabilistic exploration-exploitation trade-off is deployed. In
addition, we derive a control framework that avoids the hierarchical structure
by integrating the reference optimization in the MPC formulation. Active
learning is then performed drawing inspiration from Upper Confidence Bound
(UCB) approaches. For all developed control architectures, we guarantee
closed-loop constraint satisfaction and convergence to an optimal
configuration. Furthermore, all methods are tested and compared on hardware
using a miniature car platform.
