用散斑变化实现柔软高敏触觉感知，可识别九类纹理
A thin and soft optical tactile sensor for highly sensitive object perception
- 通过软硅胶内散斑图案变形捕捉触感，无需精密光学对准
- 力检测均方根误差40毫牛，九类纹理识别准确率93.33%
- 适合软体机器人和可穿戴触觉设备，易制备且形变适应性强
触觉感知在机器人与可穿戴设备中对安全环境交互至关重要。光学触觉传感器因其抗电磁干扰、空间分辨率高等优势成为有前景的解决方案。然而，现有光学方法，尤其是基于视觉的传感器，依赖复杂光学组件（如镜头和相机），导致设备笨重、刚性且对齐敏感。本文提出一种薄型、紧凑、柔软的无对准光学触觉传感器。该传感器通过捕获软硅胶材料内部因形变产生的散斑图案变化，结合机器学习实现精准力值测量与纹理识别。实验表明，力测量的均方根误差为40 mN，九类纹理表面（包括麻将牌）分类准确率达93.33%。所提出的基于散斑的方法提供了一种紧凑、易制备、机械柔性的平台，将光学传感与柔性可变形结构融合，展现出在软体机器人与可穿戴触觉界面中的新范式潜力。
原文摘要 · Abstract (English)
Tactile sensing is crucial in robotics and wearable devices for safe perception and interaction with the environment. Optical tactile sensors have emerged as promising solutions, as they are immune to electromagnetic interference and have high spatial resolution. However, existing optical approaches, particularly vision-based tactile sensors, rely on complex optical assemblies that involve lenses and cameras, resulting in bulky, rigid, and alignment-sensitive designs. In this study, we present a thin, compact, and soft optical tactile sensor featuring an alignment-free configuration. The soft optical sensor operates by capturing deformation-induced changes in speckle patterns generated within a soft silicone material, thereby enabling precise force measurements and texture recognition via machine learning. The experimental results show a root-mean-square error of 40 mN in the force measurement and a classification accuracy of 93.33% over nine classes of textured surfaces, including Mahjong tiles. The proposed speckle-based approach provides a compact, easily fabricated, and mechanically compliant platform that bridges optical sensing with flexible shape-adaptive architectures, thereby demonstrating its potential as a novel tactile-sensing paradigm for soft robotics and wearable haptic interfaces.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。