Robot-Assisted Vascular Shunt Insertion with the dVRK Surgical Robot

Author:

Dharmarajan Karthik1,Panitch Will1,Shi Baiyu1,Huang Huang1,Chen Lawrence Yunliang2,Moghani Masoud3,Yu Qinxi4,Hari Kush1,Low Thomas5,Fer Danyal6,Garg Animesh7,Goldberg Ken12

Affiliation:

1. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA 94709, USA

2. Department of Industrial Engineering and Operations Research, University of California, Berkeley, Berkeley, CA 94709, USA

3. Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario M5S 3G8, Canada

4. Department of Computer Science, University of Toronto, Toronto, Ontario M5S 3G8, Canada

5. Robotics Laboratory, SRI International, Menlo Park, CA 94025, USA

6. Department of General Surgery, University of California San Francisco East Bay, Oakland, CA 94602, USA

7. School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA

Abstract

Vascular shunt insertion is a common surgical procedure performed to restore blood flow to damaged tissues temporarily. It usually requires a surgeon and a surgical assistant. We consider three scenarios: (1) a surgeon is available locally; (2) a remote surgeon is available via teleoperation; (3) no surgeon is available. In each scenario, a minimally invasive surgical-assistant da Vinci robot operates in a different mode either by teleoperation or automation. Robotic assistance for this procedure is challenging due to precision and control uncertainty. The role of the robot in this task depends on the availability of a human surgeon. We propose a trimodal framework for vascular shunt insertion assisted by a da Vinci Research Kit (dVRK) robotic surgical assistant (RSA). To help further study for the community, we also present a physics-based simulated environment for shunt insertion built on top of the NVIDIA Isaac ORBIT simulator. We collect a large dataset of trajectories for the shunt insertion environment using ORBIT and implement these trajectories to show the simulator’s realism, showcasing the possibility for future work to use the simulator for policy learning. Physical experiments demonstrate a success rate of 65–100% for mode (1), 100% for mode (2), and 75–95% for mode (3) across vessel phantoms with different sizes, color, and material properties. For dataset and videos, see https://sites.google.com/berkeley.edu/ravsi .

Funder

Technology & Advanced Telemedicine Research Center

Publisher

World Scientific Pub Co Pte Ltd

Subject

Applied Mathematics,Artificial Intelligence,Computer Science Applications,Human-Computer Interaction,Biomedical Engineering

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