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

TLDR

The study introduces a mechanical needle guide and a sequential convex programming framework to automate multi‑throw suturing in robot‑assisted minimally invasive surgery. The authors implement the needle guide and optimization algorithm on a da Vinci Research Kit, testing it on tissue phantoms and benchmarking completion time against human performance from the JIGSAWS dataset. SNAP reduces needle pose estimation error by threefold versus the standard actuator, and the dVRK sutures at 30 % of human speed while achieving 86 % of attempted throws. Videos and data are available at berkeleyautomation.github.io/amts.

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.