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

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