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.

Authors

• Patrick M. Wensing
• Jean-Jacques E. Slotine

Keywords

• Adaptive control
• Manipulators
• Convergence
• Robot sensing systems
• Cloud computing
• Trajectory
• Cooperative Adaptive Control
• Network Topology
• Unknown Parameters
• Common Objects
• Cooperative Control
• Robot Manipulator
• Communication Delay
• Parameter Convergence
• Multiple Robots
• Update Law
• Inertial Parameters
• Right-hand
• Time Delay
• Right-hand Side
• Optimal Control
• Error Model
• Direct Control
• Hand Side
• Positive Definite Matrix
• Adaptive Law
• Tracking Error
• Lyapunov Function
• Integration By Parts
• Persistent Excitation
• Definite Matrix
• Communication Topology
• Error Parameters
• Moment Of Inertia
• Bidirectional Communication