Motion Planning and Control of Active Robot in Orthopedic Surgery by CDMP-Based Imitation Learning and Constrained Optimization

Abstract

Current orthopedic surgical robots are widely used in pedicle screw implantation tasks due to their precise positioning capabilities. However, the surgical operation processes, including surgical pose alignment and the drilling of the pedicle screw placement path, remain heavily dependent on the surgeon, indicating that the level of automation still needs improvement. This paper aims to enhance automation in pedicle screw implantation tasks through a combination of imitation learning and constraint optimization, thereby ensuring both reliability and safety. Firstly, a high-level motion planning method, leveraging Cartesian space dynamic movement primitives (CDMP) based imitation learning and an image-guided optical navigation system (I-GONS), is proposed to generate the task space path of the rough surgical pose alignment and fine surgical pose alignment, as well as for the drilling of pedicle screw placement path. Secondly, end-effector velocity control based on position and orientation errors (POE-EVC) is employed to follow the high-level planned path. This is achieved by constructing a quadratic programming (QP) problem with the robot kinematic constraints and manipulability optimization. Concurrently, the low-level motion control is addressed online using a modified linear variational inequality based primal dual neural network (mLVI-PDNN). Experimental results demonstrate the distance errors in multiple pedicle screw implantation tasks are <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.738\pm 0.080$ </tex-math></inline-formula> mm and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.154\pm 0.031$ </tex-math></inline-formula> mm at the entry and target points, respectively. And the angular errors between the actual drilling path compared to the planned screw placement path are <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.005\pm 0.002$ </tex-math></inline-formula> degrees. These results show that the proposed methodology offers a reliable and innovative solution for the higher level of automation in the pedicle screw implantation procedure.Note to Practitioners—The study is driven by the need for higher levels of automation in orthopedic surgery for pedicle screw implantation tasks. Orthopedic surgical robots are usually deployed in complicated surgeries due to their excellent positioning capabilities. However, they primarily rely on basic kinematic constraints and cooperative control modes, thus lacking a reliable mechanism for active surgical pose alignment and screw path drilling, especially in complex surgical scenarios that involve only optical tracking systems (OTS) without additional vision sensors. For example, current popular OTS systems have a limited field of view and cannot fully acquire information about the surgical scene, which impacts collision-free path planning. To address these issues, a novel methodology combining CDMP based imitation learning with QP based motion control for the task of pedicle screw implantation is introduced. Incorporating surgeons’ prior knowledge of the surgical environment and their participation in the robot’s motion planning using the CDMP model enhances the robot’s autonomy and increases reliability in surgeries, without requiring additional vision sensors. This approach allows for accurate reconstruction of multiple surgical targets of automatic surgical pose alignment and pedicle screw drilling, while respecting manipulability optimization and the robot’s kinematic constraints, rather than merely imitating and ignoring the robot’s kinematic structure. This methodology is also applicable to various clinical surgical operations and industrial applications.