Image-to-Force Estimation for Soft Tissue Interaction in Robotic-Assisted Surgery Using Structured Light

TL;DR

This paper presents a vision-based method using structured light and neural networks to estimate interaction forces on soft tissue during robotic surgery, addressing hardware limitations in MIS robots.

Contribution

It introduces a novel image-to-force neural network leveraging structured light and 3D point cloud analysis for soft tissue force estimation in MIS robots.

Findings

Accurate force estimation demonstrated on silicon tissue phantoms.

The method outperforms traditional force sensors in space-constrained environments.

Validated effectiveness through numerical experiments on different stiffness materials.

Abstract

For Minimally Invasive Surgical (MIS) robots, accurate haptic interaction force feedback is essential for ensuring the safety of interacting with soft tissue. However, most existing MIS robotic systems cannot facilitate direct measurement of the interaction force with hardware sensors due to space limitations. This letter introduces an effective vision-based scheme that utilizes a One-Shot structured light projection with a designed pattern on soft tissue coupled with haptic information processing through a trained image-to-force neural network. The images captured from the endoscopic stereo camera are analyzed to reconstruct high-resolution 3D point clouds for soft tissue deformation. Based on this, a modified PointNet-based force estimation method is proposed, which excels in representing the complex mechanical properties of soft tissue. Numerical force interaction experiments are conducted on three silicon materials with different stiffness. The results validate the effectiveness of the proposed scheme.