用自感知神经网络实时检测镜片装配中的卡扣到位，降低冲击力30%。
A Coordinated Dual-Arm Framework for Delicate Snap-Fit Assemblies
- 通过关节速度瞬态信号训练轻量神经网络，无需外部传感器
- 检测召回率超96%，峰值冲击力降低30%
- 双臂协同框架适合精密电子与眼镜装配场景
精密卡扣装配（如镜片装入镜框或电子元件组装）需要及时检测卡扣到位并快速衰减作用力，避免因过冲导致部件损伤或装配失败。本文提出两项关键贡献：首先，设计了SnapNet——一种轻量级神经网络，基于本体感觉信号实时检测卡扣到位，证明仅靠关节速度瞬态即可实现可靠识别；其次，构建了一种基于动力学系统的双臂协同框架，将SnapNet的事件触发检测与阻抗调节相结合，实现精准对齐与柔顺插入。在异构双臂平台上针对多种几何结构的实验表明，该方法检测召回率超过96%，峰值冲击力相比标准阻抗控制降低最多达30%。
原文摘要 · Abstract (English)
Delicate snap-fit assemblies, such as inserting a lens into an eye-wear frame or during electronics assembly, demand timely engagement detection and rapid force attenuation to prevent overshoot-induced component damage or assembly failure. We address these challenges with two key contributions. First, we introduce SnapNet, a lightweight neural network that detects snap-fit engagement from joint-velocity transients in real-time, showing that reliable detection can be achieved using proprioceptive signals without external sensors. Second, we present a dynamical-systems-based dual-arm coordination framework that integrates SnapNet driven detection with an event-triggered impedance modulation, enabling accurate alignment and compliant insertion during delicate snap-fit assemblies. Experiments across diverse geometries on a heterogeneous bimanual platform demonstrate high detection accuracy (over 96% recall) and up to a 30% reduction in peak impact forces compared to standard impedance control.
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