An Arthroscopic Robotic System for Meniscoplasty with Autonomous Operation Ability

Meniscoplasty is a common surgical procedure used to treat meniscus tears. During the operation, there are often key challenges such as a limited visual field, a narrow operating space, and difficulties in controlling the resection range. Therefore, this study developed an arthroscopic robotic system with the ability of autonomous meniscus resection to achieve better surgical outcomes. To address the issue of limited visual fields during the operation, this study used the preoperative and intraoperative meniscus point cloud images for surgical navigation and proposed a novel cross-modal point cloud registration framework. After the registration was completed, the robotic system automatically generated a resection path that could maintain the crescent shape of the remaining meniscus based on the improved Rapidly Exploring Random Tree (RRT) path-planning algorithm in this study. Meanwhile, the Remote Center of Motion (RCM) constraint was introduced during the movement of the robot to enhance safety. In this study, the mean squared error of the preoperative-intraoperative meniscus point cloud registration was only 0.1964 mm², which meets the surgical accuracy requirements. We conducted experiments to validate the autonomous operation capabilities of the robot. The robot successfully completed motion-planning and autonomous implementation, thus demonstrating the reliability of the robotic system.