Personalised Implant Path Prediction and Dynamic Motion Trajectory Smoothing for Robotic Dental Implant Surgery

BACKGROUND: The accuracy of freehand dental implant placement is highly operator-dependent, and positional and angular deviations can increase procedural risk. Robotic assistance provides improved consistency and precision in clinical implantology. METHODS: A CBCT-driven framework was developed to predict the six-degree-of-freedom position and orientation of missing teeth using multivariate regression with occlusal references. Multi-frame registration aligned imaging, simulation, and execution coordinate systems, while collision-free Cartesian trajectories were generated using RRT-Connect/RRT* and refined by seventh-order polynomial smoothing. In vitro validation was performed on high-fidelity 3D-printed jaw models. RESULTS: Mean coronal and apical deviations were 1.03 ± 0.34 and 1.06 ± 0.23 mm, respectively, with a mean angular deviation of 3.47° ± 1.99°, all within accepted clinical tolerance thresholds. CONCLUSIONS: The proposed framework enhances the accuracy and repeatability of dental implant surgery, reduces dependence on surgeon experience, and supports safer, more standardised clinical execution.