Preoperative path planning of craniotomy surgical robot based on improved MDP-LQR-RRT* algorithm

Abstract

A craniotomy is significant in treating brain diseases and has gained immense attention. The study of preoperative path planning has always been a major research issue in studying craniotomy surgical robots (CSR). Reasonable path planning can effectively prevent various brain tissue injuries and subsequent damage to the brain. Thus, to promote the efficiency of preoperative path planning and improve the safety of surgery, this study proposes a novel preoperative path planning algorithm based on improved MDP-LQR-RRT*. First, the craniotomy approach was analyzed based on expert experience, and the architecture of CSR was introduced. Subsequently, a technique for dividing the bone window area was developed to determine the shape and location of the intracranial tumor before path planning. Then, we proposed the workflow of the MDP-LQR-RRT*, which introduces a workflow that uses the LQR controller to generate path points and utilizes the Markov decision process model to refine the path. Afterward, the effectiveness and accuracy of the proposed method were verified by comparing it with other benchmark methods under two-dimensional and three-dimensional scenes. Finally, experimental verification of the skull model was carried out. The results showed that the method could realize a balanced performance compared with other methods, which provides a foundation for the clinical application of surgery while significantly improving the safety of the procedure.