2025/04/29 by Srikar Annamraju, Annamraju, Srikar, Yuxi Chen +3
Engineering · Medicine · #FOS: Electrical engineering #Soft Robotics and Applications #Surgical Simulation and Training #Systems and Control (eess.SY) #Teleoperation and Haptic Systems #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2504.20373
openalex publication_date 2025/04/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Goal: A limitation in robotic surgery is the lack of force feedback, due to challenges in suitable sensing techniques. To enhance the perception of the surgeons and precise force rendering, estimation of these forces along with tissue deformation level is presented here. Methods: An experimental test bed is built for studying the interaction, and the forces are estimated from the raw data. Since tissue deformation and stiffness are non-linearly related, they are independently computed for enhanced reliability. A Convolutional Neural Network (CNN) based vision model is deployed, and both classification and regression models are developed. Results: The forces applied on the tissue are estimated, and the tissue is classified based on its deformation. The exact deformation of the tissue is also computed. Conclusions: The surgeons can render precise forces and detect tumors using the proposed method. The rarely discussed efficacy of computing the deformation level is also demonstrated.