Passivity-Based Adaptive Control for Visually Servoed Robotic Systems
This work addresses robust visual tracking for robotic systems with uncertainties, though it appears incremental as it builds on existing passivity and adaptive control methods.
The paper tackles the visual servoing problem for robots with uncertain parameters by proposing two passivity-based adaptive control schemes, which ensure image-space tracking errors converge to zero without requiring invertible depth estimates, as validated through numerical simulations.
This paper investigates the visual servoing problem for robotic systems with uncertain kinematic, dynamic, and camera parameters. We first present the passivity properties associated with the overall kinematics of the system, and then propose two passivity-based adaptive control schemes to resolve the visual tracking problem. One scheme employs the adaptive inverse-Jacobian-like feedback, and the other employs the adaptive transpose Jacobian feedback. With the Lyapunov analysis approach, it is shown that under either of the proposed control schemes, the image-space tracking errors converge to zero without relying on the assumption of the invertibility of the estimated depth. Numerical simulations are performed to show the tracking performance of the proposed adaptive controllers.