微型触觉传感器让手术机器人同时看清和感知组织硬度。
MVP-Tac: A Miniaturized Dual-Modal Vision and Photoelastic Tactile Sensor for Robot-Assisted Minimally Invasive Surgery
- 用反射光弹性成像实现视觉与触觉共存，通过可切换模式工作。
- 在0-2牛顿范围内校准力感，对暴露和皮下肿瘤分类准确率达97%和92%。
- 适合需要兼顾视觉与触觉的微创手术机器人系统，开源设计可复现。
机器人辅助微创手术（RMIS）虽优于传统开腹与腹腔镜术式，但操作中仍缺乏触觉反馈进行触诊，且必须严格保障视觉导航与安全。实践中视觉不可或缺，无法与视觉共存的触觉方案难以应用于RMIS工具。为兼顾两者需求，本文提出MVP-Tac：一种微型化、基于视觉的双模态传感装置，实现视觉与触觉的共位感知。MVP-Tac采用反射光弹性成像技术：薄层光弹性弹性体在受力时产生应力相关干涉图样，由嵌入式相机通过微型反射偏振仪捕捉；半透明膜与可控照明使系统可在视觉与触觉模式间切换，实现触觉感知而不牺牲视觉。我们通过0至2牛顿范围内的力校准验证其性能，并在组织模拟物上基于视频完成硬度分类，实现暴露肿瘤分类97%准确率、皮下肿瘤分类92%准确率。最后，在模拟结肠镜检查中验证了在狭窄管腔内同时具备视觉引导3D壁面成像及局部结节硬度分类的能力。整体而言，MVP-Tac为恢复临床可用的触诊功能提供了可行路径，同时保持关键视觉反馈。其设计、制造与固件已在https://mvp-tac.github.io/开源。
原文摘要 · Abstract (English)
Robot-assisted minimally invasive surgery (RMIS) offers major benefits over open and conventional laparoscopic procedures, yet it still lacks tactile feedback for palpation while operating under strict requirements to preserve reliable vision for navigation and safety. In practice, visual feedback is indispensable, and tactile solutions that cannot coexist with vision are difficult to translate into RMIS tools. To address both needs, we introduce MVP-Tac, a compact, vision-based tactile sensor that provides co-located vision and tactile sensing. MVP-Tac uses reflective photoelastic imaging: a thin photoelastic elastomer produces stress-dependent interferograms under contact that are captured by an embedded camera through a miniaturized reflective polariscope. A semi-transparent membrane and controllable illumination enable switching between visual mode and tactile mode, enabling tactile perception without sacrificing vision. We validate MVP-Tac through force calibration in the 0 to 2 N range and demonstrate its potential for tumor palpation via video-based hardness classification on tissue phantoms, achieving 97% accuracy for exposed-tumor classification and 92% accuracy for subdermal-tumor classification. Finally, we conduct a simulated colonoscopy to validate both visual and tactile modalities in a constrained lumen, including vision-guided 3D photomapping of the luminal wall and in situ hardness classification of localized nodules. Overall, MVP-Tac provides a practical path toward restoring clinically useful palpation in RMIS while maintaining essential visual feedback. The design, fabrication, and firmware of MVP-Tac are open-sourced at https://mvp-tac.github.io/
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。