  In this paper we present a reformulation--framed as a constrained
optimization problem--of multi-robot tasks which are encoded through a cost
function that is to be minimized. The advantages of this approach are multiple.
The constraint-based formulation provides a natural way of enabling long-term
robot autonomy applications, where resilience and adaptability to changing
environmental conditions are essential. Moreover, under certain assumptions on
the cost function, the resulting controller is guaranteed to be decentralized.
Furthermore, finite-time convergence can be achieved, while using local
information only, and therefore preserving the decentralized nature of the
algorithm. The developed control framework has been tested on a team of ground
mobile robots implementing long-term environmental monitoring.
