Admittance-Based Surface Alignment for Human-in-the-Loop Robotic Visual Inspection

Robotic visual inspection requires precise alignment between the end-effector and local surface geometry in the presence of perception noise and surface irregularities. In industrial settings, a human operator is often kept in the loop via teleoperation or shared autonomy, introducing real-time adjustments that render purely offline motion planning inadequate. This motivates control architectures capable of reactive, compliant behavior under combined human and perceptual uncertainty. This paper presents a novel robotic inspection framework with admittance-based control that unifies operator input and perception-driven surface alignment. The end-effector is modeled as a virtual sphere moving through a viscous medium, yielding a physically interpretable mass–damper system that generates synchronized, compliant motion from orientation error and operator commands. We validate the framework on a 6-DOF manipulator demonstrating stable normal-tracking and a final mean orientation error of 0.007 rad.

Journal article