Simulating Surgical Robot Cutting of Thin Deformable Materials Using a Rope Grid Structure

Abstract

Traditional methods for autonomous cutting in surgical robotics have relied on trajectory-based planning algorithms. These methods fail to compensate for dynamic changes in soft materials such as deformation and topological change. To apply recent advances such as reinforcement learning (RL), a simulation is needed that models the cutting of soft materials. In this work, we develop a surgical robotics simulation environment for cutting deformable meshes with the da Vinci Research Kit (dVRK). Our environment is built using a particle-based physics simulation to simulate a rope grid structure to create realistic physics behavior and visual rendering. Cutting is implemented with the EndoWrist Round Tip Scissors (RTS) through a system of collision checking and callbacks to detect and update cuts. To showcase the deformable mesh cutting simulation, we design a cutting task of cutting along a desired path that can be solved through manual control. The grid structure can be adapted to render different materials, and we highlight how it can be made to resemble deformable tissue or fabric while being stable with no visible artifacts. This environment is a stepping stone towards training autonomous agents for cutting 2D deformable materials and building towards cutting more complex deformable shapes.