Cooperative Adaptive Control for Cloud-Based Robotics
This work addresses the challenge of improving parameter estimation in cooperative robotics for applications like manipulation, though it appears incremental by extending adaptive control to networked conditions.
The paper tackles the problem of enabling multiple robots to collaboratively identify unknown inertial parameters of a common object through cloud-based adaptive control, introducing a concept called Collective Sufficient Richness that allows parameter convergence through teamwork, with simulations demonstrating its benefits.
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.