Decentralized Adaptive Control for Collaborative Manipulation

This paper presents a design for a decentralized adaptive controller that allows a team of agents to manipulate a common payload in $\mathbb{R}^{2}$ or $\mathbb{R}^{3}$. The controller requires no communication between agents and requires no a priori knowledge of agent positions or payload properties. The agents can control the payload to track a reference trajectory in linear and angular velocity with center-of-mass measurements, in angular velocity using only local measurements and a common frame, and can stabilize its rotation with only local measurements. The controller is designed via a Lyapunov-style analysis and has proven stability and convergence. The controller is validated in simulation and experimentally with four robots manipulating an object in the plane.

Authors

• Preston Culbertson
• Mac Schwager

Keywords

• Payloads
• Robots
• Collaboration
• Angular velocity
• Velocity measurement
• Task analysis
• Stability analysis
• Adaptive Control
• Collaborative Manipulation
• Center Of Mass
• Local Measurements
• Linear Velocity
• Common Frame
• Knowledge Of Position
• Nonlinear Systems
• Gain Control
• Reference Model
• Reference Signal
• Velocity Measurements
• Friction Force
• Manipulation Tasks
• Adaptive Law
• Class Of Nonlinear Systems
• Ground Station
• Unknown Matrix
• Swarm Robotics
• Static Friction
• Body-fixed Frame
• Removal Of Agents
• Asymptotic Tracking
• Persistent Excitation
• Class Of Dynamical Systems
• Frictional Properties
• Knowledge Of Parameters
• Wireless Networks