A Full-Range Proximity-Tactile Sensor Based on Multimodal Perception Fusion for Minimally Invasive Surgical Robots.

Journal: Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Published Date: Jun 19, 2025

Abstract

Minimally invasive surgical robots have received widespread attention due to its numerous advantages. However, the lack of adequate perception capability remains a significant issue for the robots. In this work, a full-range proximity-tactile sensing module has been developed for safe operation of surgical robots, which performs multimodal fusion perception through ultrasonic sensor for long-range proximity detection, capacitive sensor for close-range proximity sensing, and triboelectric sensor for tactile sensing. In order for a minimum sensor size, the ultrasonic sensor is developed based on MEMS piezoelectric micromachined ultrasonic transducers (pMUTs), and the capacitive sensor and triboelectric sensor adopt common structures, which collaborate to achieve accurate proximity-tactile perception. Additionally, a wireless vibration feedback wristband and digital-twin interface are developed to provide multimodal feedback without interfering with operation. Experimental results demonstrates the safety enhancement for surgical robots by the perception and feedback system. Furthermore, the sensing module is applied in preliminary detection of subcutaneous abnormal tissues and the identification accuracy based on the ultrasound echoes and convolutional neural networks is 91.6%, which can provide an initial diagnostic reference. The full-range proximity-tactile sensor holds significant potential for enhancing the safety and detection capability of surgical robots, and promoting the intelligence of robot-assisted minimally invasive surgery.

Authors

  • Dongsheng Li
    Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Tianci Ji
    School of Mechanical and Electrical Engineering, Jiangsu Key Laboratory of Embodied Intelligence Robot Technology, Soochow University, Suzhou, 215137, China.
  • Yuyang Sun
    School of Computer, Jiangsu University of Science and Technology Zhenjiang 212100, Jiangsu, China.
  • Zhongbin Zhang
    School of Mechanical and Electrical Engineering, Jiangsu Key Laboratory of Embodied Intelligence Robot Technology, Soochow University, Suzhou, 215137, China.
  • Aomen Li
    School of Mechanical and Electrical Engineering, Jiangsu Key Laboratory of Embodied Intelligence Robot Technology, Soochow University, Suzhou, 215137, China.
  • Mengjiao Qu
    State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, Zhejiang, 310027, China.
  • Dongze Lv
    State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, Zhejiang, 310027, China.
  • Jin Xie
    School of Mathematics and Statistics, Xidian University, Xi'an 710071, PR China. Electronic address: xj6417@126.com.
  • Huicong Liu
    School of Mechanical and Electric Engineering, Collaborative Innovation Center of Suzhou Nano Science and Technology, Jiangsu Provincial Key Laboratory of Advanced Robotics, Soochow University, Suzhou 215000, China. hcliu078@suda.edu.cn.

Keywords

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