让机器人在人机协同下实时贴合曲面，精度达0.4度。
Admittance-Based Surface Alignment for Human-in-the-Loop Robotic Visual Inspection
- 用虚拟阻尼球模型融合操作员指令与视觉对齐信号。
- 6自由度机械臂实现稳定法向跟踪，平均姿态误差仅0.4°。
- 适合需要高精度、人机协作的工业质检场景。
高精度视觉检测是航空航天、半导体和医疗制造领域质量保障的关键，未发现的表面缺陷会导致报废、返工和现场故障。机器人视觉检测需在感知噪声和表面不规则条件下，实现末端执行器与局部表面几何的精确对齐。工业场景中常通过遥操作或共享自主性保留人工干预，实时调整使纯离线运动规划失效。因此亟需具备响应性、柔顺性的控制架构以应对人因与感知不确定性。本文提出一种新型实时闭环机器人姿态控制流程，采用阻抗基框架统一操作者输入与感知驱动的表面对齐。将末端执行器建模为在黏性介质中运动的虚拟球体，形成具物理可解释性的质-阻系统，从而从姿态误差与操作指令中生成同步、柔顺的运动。在6自由度机械臂上验证该框架，实现稳定法向跟踪，最终平均姿态误差为0.4°。
原文摘要 · Abstract (English)
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°.
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