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.

翻译：本文提出一种分布式算法，用于控制一组协作机器人以保护目标免受入侵者侵扰。具体而言，我们借鉴新兴的分布式聚合框架，通过在线优化问题对防御团队的策略进行建模。特别地，每个防御机器人根据以下因素确定自身位置：(i) 关联入侵者与目标之间的相对位置，(ii) 其对团队质心的贡献度，以及(iii) 与队友间的避碰需求。我们强调，每个智能体仅能获取关于关联入侵者及目标位置的局部含噪测量值。因此，在每个机器人中，我们的算法需要：(i) 局部重构全局不可获取的量，以及(ii) 基于局部测量值预测其当前目标函数。通过协作四旋翼机团队的仿真与实验，验证了所提方法的有效性。