Image-to-Force Estimation for Soft Tissue Interaction in Robotic-Assisted Surgery Using Structured Light
This addresses the challenge of providing accurate haptic feedback for soft tissue interaction in minimally invasive surgical robots, which is incremental as it builds on existing vision and neural network methods.
The paper tackles the problem of estimating interaction forces in robotic-assisted surgery without hardware sensors by using a vision-based scheme with structured light and a neural network, achieving validated effectiveness on silicon materials with different stiffness.
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.