Cooperative Adaptive Control for Cloud-Based Robotics

This paper studies collaboration through the cloud in the context of cooperative adaptive control for robot manipulators. We first consider the case of multiple robots manipulating a common object through synchronous centralized update laws to identify unknown inertial parameters. Through this development, we introduce a notion of Collective Sufficient Richness, wherein parameter convergence can be enabled through teamwork in the group. The introduction of this property and the analysis of stable adaptive controllers that benefit from it constitute the main new contributions of this work. Building on this original example, we then consider decentralized update laws, time-varying network topologies, and the influence of communication delays on this process. Perhaps surprisingly, these nonidealized networked conditions inherit the same benefits of convergence being determined through collective effects for the group. Simple simulations of a planar manipulator identifying an unknown load are provided to illustrate the central idea and benefits of Collective Sufficient Richness.Comment: ICRA 201

Towards minimum-information adaptive controllers for robot manipulators

The aim of this paper is to move a step in the direction of determining the minimum amount of information needed to control a robot manipulator within the framework of adaptive control. Recent innovations in the state of the art show how global asymptotic trajectory tracking can be achieved despite the presence of uncertainties in the kinematic and dynamic models of the robot. However, a clear distinction between which parameters can be included among the uncertainties, and which parameters can not, has not been drawn yet. Since most of the adaptive control algorithms are built on linearly parameterized models, we propose to reformulate the problem as finding a procedure to determine whether and how a given dynamical system can be linearly parameterized with respect to a specific set of parameters. Within this framework, we show how the trajectory tracking problem of a manipulator can be accomplished with the only knowledge of the number of joints of the manipulator. As an illustrative example, we present the end-effector trajectory tracking control of a robot initialized with the kinematic model of a different robot