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

2026-06-16

AI Summary

This study addresses the challenge of achieving precise alignment between the end-effector and local surface geometry in human–robot collaborative visual inspection, which is hindered by perceptual noise and surface irregularities. To this end, the authors propose a real-time closed-loop pose control framework based on admittance control. The approach innovatively models the end-effector as a virtual sphere immersed in a viscous medium, thereby constructing a physically interpretable mass–damping system that simultaneously integrates human operator commands with perception-driven surface alignment. Experimental validation on a six-degree-of-freedom robotic arm demonstrates stable normal-direction tracking with an average orientation error as low as 0.4°, confirming the effectiveness of the proposed shared autonomy strategy for high-precision visual inspection tasks.

Abstract

Precision visual inspection underpins quality assurance across aerospace, semiconductor, and medical manufacturing, where undetected surface anomalies on high-value parts translate directly into scrap, rework, and field failures. 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 real-time, closed-loop robotic orientation control pipeline for precision visual inspection, with an admittance-based framework that unifies operator input and perception-driven surface alignment. We design the end-effector as a virtual sphere moving through a viscous medium, such that the resulting physically interpretable mass--damper system 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 0.4°.

Problem

• surface alignment
• human-in-the-loop
• robotic visual inspection
• perception uncertainty
• compliant control

Innovation

• admittance control
• surface alignment
• human-in-the-loop
• compliant motion
• robotic visual inspection