SCANS：一款搭载曲率与光谱传感器、用于手中材料区分的柔性夹爪
SCANS: A Soft Gripper with Curvature and Spectroscopy Sensors for In-Hand Material Differentiation
AI总结：
本研究提出无电子、流体驱动的SCANS柔性夹爪系统，集成曲率与光谱传感，可在预接触和握持状态下区分多类材料，验证了近红外波段的关键作用，推动光学在柔性机器人中的多功能感知应用。
摘要：
我们推出柔性曲率与光谱（SCANS）系统：这是一款多功能、无电子元件、流体驱动的柔性操作器，可在握持物体或预接触围合状态下评估物体的光谱特性。该平台比以往的柔性机器人同类设备具备更广泛的光谱感知能力。我们开展了材料分析，以探索适用于光谱感知的最优柔性基底，并评估了预接触和握持两种模式下的性能。实验表明，该系统可对不同类别和尺寸的物体（金属、木材、塑料、有机物、纸张、泡沫）实现可解释的统计学区分，物体间的光谱角差异较大。通过线性判别分析我们发现，近红外波长的灵敏度对于区分视觉相似的物体至关重要。这些性能提升了光学技术作为柔性机器人多功能感知模态的潜力。SCANS夹爪的完整零件清单、装配指南和处理代码可访问：https://parses-lab.github.io/scans/。
英文摘要：
We introduce the soft curvature and spectroscopy (SCANS) system: a versatile, electronics-free, fluidically actuated soft manipulator capable of assessing the spectral properties of objects either in hand or through pre-touch caging. This platform offers a wider spectral sensing capability than previous soft robotic counterparts. We perform a material analysis to explore optimal soft substrates for spectral sensing, and evaluate both pre-touch and in-hand performance. Experiments demonstrate explainable, statistical separation across diverse object classes and sizes (metal, wood, plastic, organic, paper, foam), with large spectral angle differences between items. Through linear discriminant analysis, we show that sensitivity in the near-infrared wavelengths is critical to distinguishing visually similar objects. These capabilities advance the potential of optics as a multi-functional sensory modality for soft robots. The complete parts list, assembly guidelines, and processing code for the SCANS gripper are accessible at: https://parses-lab.github.io/scans/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
