融合视觉触觉与力矩传感，提升机器人理疗触诊对深层组织特征的识别能力
Multimodal Sensing for Robot-Assisted Sub-Tissue Feature Detection in Physiotherapy Palpation
- 集成高分辨率触觉成像与六维力矩传感器，实现多模态感知
- 在硅胶假体中，仅靠力信号常出现误判，而触觉图像可清晰分辨肌腱结构差异
- 适合需要精准触诊反馈的康复机器人研究与临床辅助系统开发
机器人触诊依赖力信号，但在软组织环境中力信号变化大，难以可靠揭示细微的深层结构特征。本文提出一种紧凑型多模态传感器，集成高分辨率视觉触觉成像与六维力-扭矩传感器。在具有不同深层肌腱几何结构的硅胶假体实验中，仅依靠力信号常导致响应模糊，而触觉图像能清晰呈现结构的存在性、直径、深度、交叉关系及多重性差异。然而，精确的力跟踪对于维持理疗交互过程中的安全、稳定接触仍至关重要。初步结果表明，融合触觉与力信号可实现鲁棒的深层特征检测，并支持受控的机器人触诊。
原文摘要 · Abstract (English)
Robotic palpation relies on force sensing, but force signals in soft-tissue environments are variable and cannot reliably reveal subtle subsurface features. We present a compact multimodal sensor that integrates high-resolution vision-based tactile imaging with a 6-axis force-torque sensor. In experiments on silicone phantoms with diverse subsurface tendon geometries, force signals alone frequently produce ambiguous responses, while tactile images reveal clear structural differences in presence, diameter, depth, crossings, and multiplicity. Yet accurate force tracking remains essential for maintaining safe, consistent contact during physiotherapeutic interaction. Preliminary results show that combining tactile and force modalities enables robust subsurface feature detection and controlled robotic palpation.
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