2020/05/06 by Preston Culbertson, Jean-Jacques Slotine, Culbertson, Preston +3 · 5 citations
Computer Science · Engineering · #Distributed Control Multi-Agent Systems #Advanced Control Systems Optimization #Robot Manipulation and Learning
paper · pdf · doi:10.48550/arxiv.2005.03153
In this work, we consider a group of robots working together to manipulate a\nrigid object to track a desired trajectory in SE(3). The robots do not know\nthe mass or friction properties of the object, or where they are attached to\nthe object. They can, however, access a common state measurement, either from\none robot broadcasting its measurements to the team, or by all robots\ncommunicating and averaging their state measurements to estimate the state of\ntheir centroid. To solve this problem, we propose a decentralized adaptive\ncontrol scheme wherein each agent maintains and adapts its own estimate of the\nobject parameters in order to track a reference trajectory. We present an\nanalysis of the controller's behavior, and show that all closed-loop signals\nremain bounded, and that the system trajectory will almost always (except for\ninitial conditions on a set of measure zero) converge to the desired\ntrajectory. We study the proposed controller's performance using numerical\nsimulations of a manipulation task in 3D, as well as hardware experiments which\ndemonstrate our algorithm on a planar manipulation task. These studies, taken\ntogether, demonstrate the effectiveness of the proposed controller even in the\npresence of numerous unmodeled effects, such as discretization errors and\ncomplex frictional interactions.\n