MVP-Tac：一种用于机器人辅助微创手术的小型化双模态视觉与光弹性触觉传感器
MVP-Tac: A Miniaturized Dual-Modal Vision and Photoelastic Tactile Sensor for Robot-Assisted Minimally Invasive Surgery
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中文总结 AI 辅助
研究针对机器人辅助微创手术缺乏触觉反馈问题，介绍了MVP-Tac传感器，它采用反射光弹性成像，能在视觉触觉间切换，经力校准及肿瘤触诊等实验验证其有效性，为恢复RMIS中触诊并保持视觉反馈提供实用途径。
中文摘要 AI 辅助
机器人辅助微创手术（RMIS）比开放手术和传统腹腔镜手术有诸多优势，但在操作时缺乏触觉反馈，同时需保留可靠视觉用于导航和安全。为满足视觉和触觉需求，我们引入了MVP-Tac，一种紧凑的基于视觉的触觉传感器，可提供共定位视觉和触觉传感。它采用反射光弹性成像，通过小型反射偏光镜由嵌入式相机捕获接触时产生的应力干涉图。半透明膜和可控照明实现视觉模式和触觉模式切换。我们通过在0至2N范围内的力校准验证了MVP-Tac，并通过对组织模型进行基于视频的硬度分类展示其在肿瘤触诊方面的潜力，暴露肿瘤分类准确率达97%，皮下肿瘤分类准确率达92%。最后，我们进行了模拟结肠镜检查，验证了在受限管腔中的视觉和触觉模式。总体而言，MVP-Tac为在RMIS中恢复临床有用的触诊同时保持基本视觉反馈提供了一条实用途径。MVP-Tac的设计、制造和固件已在该https URL开源。
英文摘要
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/
发表机构
- Elmore Family School of Electrical and Computer Engineering, Purdue University（普渡大学埃尔莫尔电气与计算机工程学院）
- Edwardson School of Industrial Engineering, Purdue University（普渡大学爱德华森工业工程学院）
- School of Mechanical Engineering, Purdue University（普渡大学机械工程学院）
- Robotics Engineering Technology, Purdue University（普渡大学机器人工程技术学院）
- Department of Civil Engineering, Stony Brook University（石溪大学土木工程系）
- Department of Mechanical Engineering, Michigan State University（密歇根州立大学机械工程系）
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