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

A Distributed Online Optimization Strategy for Cooperative Robotic Surveillance