Applying Depth-Sensing to Automated Surgical Manipulation with a da Vinci Robot
This work addresses automation of surgical tasks for robotic surgery, but it is incremental as it builds on existing depth-sensing and calibration methods.
The study tackled automating surgical subtasks using depth-sensing with a da Vinci robot, achieving reliability of 86.9% and 78.0% for block transfers with speeds of 10.02 and 5.72 seconds, while reducing error from 4-5 mm to 1-2 mm through calibration.
Recent advances in depth-sensing have significantly increased accuracy, resolution, and frame rate, as shown in the 1920x1200 resolution and 13 frames per second Zivid RGBD camera. In this study, we explore the potential of depth sensing for efficient and reliable automation of surgical subtasks. We consider a monochrome (all red) version of the peg transfer task from the Fundamentals of Laparoscopic Surgery training suite implemented with the da Vinci Research Kit (dVRK). We use calibration techniques that allow the imprecise, cable-driven da Vinci to reduce error from 4-5 mm to 1-2 mm in the task space. We report experimental results for a handover-free version of the peg transfer task, performing 20 and 5 physical episodes with single- and bilateral-arm setups, respectively. Results over 236 and 49 total block transfer attempts for the single- and bilateral-arm peg transfer cases suggest that reliability can be attained with 86.9 % and 78.0 % for each individual block, with respective block transfer speeds of 10.02 and 5.72 seconds. Supplementary material is available at https://sites.google.com/view/peg-transfer.