  In this paper, we propose a distributed algorithm to control a team of
cooperating robots aiming to protect a target from a set of intruders.
Specifically, we model the strategy of the defending team by means of an online
optimization problem inspired by the emerging distributed aggregative
framework. In particular, each defending robot determines its own position
depending on (i) the relative position between an associated intruder and the
target, (ii) its contribution to the barycenter of the team, and (iii)
collisions to avoid with its teammates. We highlight that each agent is only
aware of local, noisy measurements about the location of the associated
intruder and the target. Thus, in each robot, our algorithm needs to (i)
locally reconstruct global unavailable quantities and (ii) predict its current
objective functions starting from the local measurements. The effectiveness of
the proposed methodology is corroborated by simulations and experiments on a
team of cooperating quadrotors.
