Research in surgical robotics and automation has made remarkable advancements in recent years thanks to new methods in computer vision, control, and deep learning. Autonomous end-effector manipulation is a challenging task in surgical robotics, and cutting with scissor tools is largely unexplored. A concurrent work explored path and trajectory generation for cutting deformable materials using the da Vinci Research Kit (dVRK) [1]. However, an efficient and realistic simulation is necessary for methods such as reinforcement learning (RL) or learned trajectory planning. Our previous work built a simulation for the dVRK in Unity for training RL algorithms on rigid body tasks [2]. To our knowledge, there is no dVRK simulation available that includes the cutting of deformable materials. This paper introduces a cutting simulation of a deformable mesh, which can represent a tissue layer, built onto our Unity dVRK simulation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.