Enhancing Surgical Precision in Autonomous Robotic Incisions via Physics-Based Tissue Cutting Simulation.

In soft tissue surgeries, such as tumor resections, achieving precision is of utmost importance. Surgeons conventionally achieve this precision through intraoperative adjustments to the cutting plan, responding to deformations from tool-tissue interactions. This study examines the integration of physics-based tissue cutting simulations into autonomous robotic surgery to preoperatively predict and compensate for such deformations, aiming to improve surgical precision and reduce the necessity for dynamic adjustments during autonomous surgeries. This study adapts a real-to-sim-to-real workflow. Initially, the Autonomous System for Tumor Resection (ASTR) was employed to evaluate its accuracy in performing preoperatively intended incisions along the irregular contours of porcine tongue pseudotumors. Following this, a finite element analysis-based simulation, utilizing the Simulation Open Framework Architecture (SOFA), was developed and tuned to accurately mimic these tissue and incision interactions. Insights gained from this simulation were applied to refine the robot's path planning, ensuring a closer alignment of actual incisions with the initially intended surgical plan. The efficacy of this approach was validated by comparing surface incision precision on