Cooperative Adaptive Control for Cloud-Based Robotics

TL;DR

This paper introduces a cooperative adaptive control framework for cloud-based robotics, emphasizing collective parameter convergence through teamwork, even under decentralized, delayed, or changing network conditions.

Contribution

It presents the novel concept of Collective Sufficient Richness, enabling parameter convergence via teamwork in cooperative adaptive control for robots.

Findings

Collective Sufficient Richness facilitates parameter convergence in multi-robot systems.

Stable adaptive controllers benefit from collective effects even with network delays.

Simulations demonstrate effective load identification in robotic manipulators.

Abstract

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.