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

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 0.738 +/- 0.080 mm and 0.154 +/- 0.031 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 0.005 +/- 0.002 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.