In this work, we consider a group of robots working together to manipulate a rigid object to track a desired trajectory in $SE(3)$. The robots do not know the mass or friction properties of the object, or where they are attached to the object. They can, however, access a common state measurement, either from one robot broadcasting its measurements to the team, or by all robots communicating and averaging their state measurements to estimate the state of their centroid. To solve this problem, we propose a decentralized adaptive control scheme wherein each agent maintains and adapts its own estimate of the object parameters in order to track a reference trajectory. We present an analysis of the controller's behavior, and show that all closed-loop signals remain bounded, and that the system trajectory will almost always (except for initial conditions on a set of measure zero) converge to the desired trajectory. We study the proposed controller's performance using numerical simulations of a manipulation task in 3D, as well as hardware experiments which demonstrate our algorithm on a planar manipulation task. These studies, taken together, demonstrate the effectiveness of the proposed controller even in the presence of numerous unmodeled effects, such as discretization errors and complex frictional interactions.

翻译：在这项工作中,我们考虑一组机器人,他们一起操纵一个僵硬的物体,以追踪所需的轨迹,用美元(3)美元计算。机器人不知道物体的质量或摩擦性质,或它们与物体的附着之处。但是,他们可以从一个机器人向小组播放其测量结果,或从所有机器人交流和平均状态测量来估计其半机器人的状态。为了解决这个问题,我们提议一个分散的适应性控制方案,让每个代理人维持和调整自己对物体参数的估计,以便跟踪参考轨迹。我们分析了控制者的行为,并表明所有闭路信号仍然被捆绑,而且系统轨迹几乎总是会(除了一套零度测量的初始条件之外)与理想轨迹一致。我们用3D操纵任务的数字模拟以及硬件实验来研究拟议的控制者的性能,这些实验显示了我们关于规划操纵任务的算法。这些研究一起显示,即使在存在无数未建模效果的情况下,例如离散错误和复杂摩擦,拟议的控制者的有效性。