Kanishkan Senthilkumar, R. Gondokaryono, Mustafa Haiderbhai, L. Kahrs
{"title":"Simulating Mesh Cutting with the dVRK in Unity","authors":"Kanishkan Senthilkumar, R. Gondokaryono, Mustafa Haiderbhai, L. Kahrs","doi":"10.31256/hsmr2023.70","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":129686,"journal":{"name":"Proceedings of The 15th Hamlyn Symposium on Medical Robotics 2023","volume":"47 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of The 15th Hamlyn Symposium on Medical Robotics 2023","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31256/hsmr2023.70","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
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.