Open-source High-precision Autonomous Suturing Framework With Visual Guidance

TL;DR

This paper presents an open-source, high-precision autonomous suturing framework using visual guidance, combining geometric algorithms and neural networks to improve surgical robot accuracy and performance.

Contribution

It introduces a novel image-based framework with an algebraic geometric algorithm and keypoint calibration network for precise autonomous suturing.

Findings

Ranked first in the AccelNet Surgical Robotics Challenge

Achieved high-precision needle pose estimation

Demonstrated effective joint-offset compensation and trajectory planning

Abstract

Autonomous surgery has attracted increasing attention for revolutionizing robotic patient care, yet remains a distant and challenging goal. In this paper, we propose an image-based framework for high-precision autonomous suturing operation. We first build an algebraic geometric algorithm to achieve accurate needle pose estimation, then design the corresponding keypoint-based calibration network for joint-offset compensation, and further plan and control suture trajectory. Our solution ranked first among all competitors in the AccelNet Surgical Robotics Challenge. Videos and codes can be found in https://sites.google.com/view/accel-2022-cuhk.