Automatic Robotic Cranium-Milling: A Motion Control Study of In Vitro Animal Experiments

Abstract

Autonomous robotic surgery offers enhanced effectiveness, precision, and reliability, regardless of the surgeons’ expertise. Prior neurosurgery robot studies involved surgeons manually assisting the robot in skull-milling tasks by holding the milling cutter shank, constraining the robot’s autonomy. A model-free adaptive nonlinear force control algorithm is designed to accomplish automatic cranial-milling tasks. Furthermore, a skull-milling breakthrough detection algorithm by monitoring the change of feed force is proposed to determine the completion of the milling task autonomously. A robotic system is developed for automatic cranium-milling and 72 in vitro skull-milling experiments indicate that when using the proposed control algorithm, the maximum root mean square error percentage of the vertical force is 0.99 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\%$</tex-math> </inline-formula> , while the control error percentages of other mainstream methods are all above 5.5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\%$</tex-math> </inline-formula> . Moreover, the success rate of breakthrough detection is 98.61 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\%$</tex-math> </inline-formula> and the robot autonomously performs the skull milling task with minimal human intervention during the whole experiment. The results demonstrate that the proposed method provides the potential to improve the intelligence of neurosurgery. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> — The purpose is to propose a model-free adaptive nonlinear force control method for automatic skull-milling tasks. In previous studies, the involvement of surgeons manually holding the milling cutter shank to assist neurosurgery robots in skull-milling tasks has been observed. However, the autonomy of the robot is restricted and its potential for precise control tasks is failed to leverage. Therefore, a model-free adaptive nonlinear force control algorithm is proposed and a robotic system is built to enable the robot to autonomously perform cranial-milling tasks in this work. This application aims to enhance the autonomy of robot-assisted neurosurgery, making it a potential solution for remote surgery and addressing the shortage of medical resources in rural areas.