An open-source research kit for the da Vinci® Surgical System

Kazanzides, Peter; Chen, Zihan; Deguet, Anton; Fischer, Gregory S.; Taylor, Russell H.; DiMaio, Simon P.

doi:10.1109/icra.2014.6907809

Cited by 439 publications

(258 citation statements)

References 9 publications

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“…We use the Intuitive Surgical da Vinci Research Kit (dVRK) surgical robot assistant as in [21], along with opensource electronics and software developed by WPI and Johns Hopkins University [12]. We use a pair of 8mm Needle Drivers with each gripper having one Suture Needle Angular Positioner (SNAP).…”

Section: A Dvrk: Hardware and Softwarementioning

confidence: 99%

Automating multi-throw multilateral surgical suturing with a mechanical needle guide and sequential convex optimization

Sen

Garg

Gealy

et al. 2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

157

101

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Abstract-For supervised automation of multi-throw suturing in Robot-Assisted Minimally Invasive Surgery, we present a novel mechanical needle guide and a framework for optimizing needle size, trajectory, and control parameters using sequential convex programming. The Suture Needle Angular Positioner (SNAP) results in a 3x error reduction in the needle pose estimate in comparison with the standard actuator. We evaluate the algorithm and SNAP on a da Vinci Research Kit using tissue phantoms and compare completion time with that of humans from the JIGSAWS dataset [5]. Initial results suggest that the dVRK can perform suturing at 30% of human speed while completing 86% suture throws attempted. Videos and data are available at: berkeleyautomation.github.io/amts

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Section: A Dvrk: Hardware and Softwarementioning

confidence: 99%

Automating multi-throw multilateral surgical suturing with a mechanical needle guide and sequential convex optimization

Sen

Garg

Gealy

et al. 2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

157

101

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“…We use the da Vinci Research Kit (DVRK) [15,16], a research platform built from mechanical components from the first-generation da Vinci robotic surgical system [4] and electronics and software from WPI and Johns Hopkins University.…”

Section: Introductionmentioning

confidence: 99%

Learning by observation for surgical subtasks: Multilateral cutting of 3D viscoelastic and 2D Orthotropic Tissue Phantoms

Murali

Sen

Kehoe

et al. 2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

162

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Abstract-Automating repetitive surgical subtasks such as suturing, cutting and debridement can reduce surgeon fatigue and procedure times and facilitate supervised tele-surgery. Programming is difficult because human tissue is deformable and highly specular. Using the da Vinci Research Kit (DVRK) robotic surgical assistant, we explore a "Learning By Observation" (LBO) approach where we identify, segment, and parameterize sub-trajectories ("surgemes") and sensor conditions to build a finite state machine (FSM) for each subtask. The robot then executes the FSM repeatedly to tune parameters and if necessary update the FSM structure. We evaluate the approach on two surgical subtasks: debridement of 3D Viscoelastic Tissue Phantoms (3d-DVTP), in which small target fragments are removed from a 3D viscoelastic tissue phantom, and Pattern Cutting of 2D Orthotropic Tissue Phantoms (2d-PCOTP), a step in the standard Fundamentals of Laparoscopic Surgery training suite, in which a specified circular area must be cut from a sheet of orthotropic tissue phantom. We describe the approach and physical experiments, which yielded a success rate of 96% for 50 trials of the 3d-DVTP subtask and 70% for 20 trials of the 2d-PCOTP subtask.

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“…A n sin(ω n t) + B n cos(ω n t) (13) to the one-dimensional signal. In (13),p i (t), i = x, y, z denote the coordinates of the predicted position…”

Section: B Motion Predictionmentioning

confidence: 99%

“…4) using the da Vinci Research Kit (dVRK) [13]. Its effectiveness is demonstrated by the conducted user studies simulating surface scanning for in situ medical imaging and vessel manipulation in cardiac surgery.…”

Section: Introductionmentioning

confidence: 99%

Motor channelling for safe and effective dynamic constraints in Minimally Invasive Surgery

Grammatikopoulou

Leibrandt

Yang

2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-Motor channelling is a concept to provide navigation and sensory feedback to operators in master-slave surgical setups. It is beneficial since the introduction of robotic surgery creates a physical separation between the surgeon and patient anatomy. Active Constraints/Virtual Fixtures are proposed which integrate Guidance and Forbidden Region Constraints into a unified control framework. The developed approach provides guidance and safe manipulation to improve precision and reduce the risk of inadvertent tissue damage. Online three-degree-of-freedom motion prediction and compensation of the target anatomy is performed to complement the master constraints. The presented Active Constraints concept is applied to two clinical scenarios; surface scanning for in situ medical imaging and vessel manipulation in cardiac surgery. The proposed motor channelling control strategy is implemented on the da Vinci Surgical System using the da Vinci Research Kit (dVRK) and its effectiveness is demonstrated through a detailed user study.

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An open-source research kit for the da Vinci® Surgical System

Cited by 439 publications

References 9 publications

Automating multi-throw multilateral surgical suturing with a mechanical needle guide and sequential convex optimization

Automating multi-throw multilateral surgical suturing with a mechanical needle guide and sequential convex optimization

Learning by observation for surgical subtasks: Multilateral cutting of 3D viscoelastic and 2D Orthotropic Tissue Phantoms

Motor channelling for safe and effective dynamic constraints in Minimally Invasive Surgery

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