In-Situ Automated Robotic Crown Preparation with MPC-Based Adaptive Control
Heng Liu, Huayu Fang, Shizhu Bai, Yimin Zhao, Junchen Wang
AI summary
Problem
Current robotic and guided crown preparation methods struggle with inefficient tool paths, inadequate collision avoidance in the confined oral cavity, and rigid constant feed rates that ignore real-time cutting conditions.
Approach
The system generates efficient cutting paths based on tooth morphology, optimizes tool orientation to prevent collisions with surrounding teeth, and uses model predictive control to dynamically adjust cutting speed based on real-time force and position feedback.
Key results
- Morphology-driven path planning eliminates toolpath ridges
- Quadratic programming optimization ensures collision-free intraoral motion
- MPC controller dynamically modulates feed rate for 0.17 mm RMS accuracy
- 74.2% improvement in cutting efficiency over state-of-the-art methods
Why it matters
This system bridges the gap between robotic innovation and clinical practice by enabling safer, faster, and highly precise automated crown preparations for dentists and dental engineers.
Abstract
Crown preparation aims to create an optimal foundation for durable and functional restoration by reshaping the tooth with a cutting tool. Robotic crown preparation has emerged as a promising approach to overcome the inherent limitations of manual procedures, yet challenges remain in achieving efficient cutting path generation, collision-free ori- entation adjustment and precise cutting path following, since the oral cavity is a confined space with the target tooth tightly surrounded by other teeth. This paper introduces a novel, in-situ automated robotic full crown preparation system comprising (1) Preoperative Path Planning: generating high- efficiency universal cutting paths based on tooth morphological features; (2) Intraoral Collision Avoidance: optimizing the cutting tool’s orientation within the constrained oral cavity; (3) MPC-Based Adaptive Control: modulating the path-following feed rate using model predictive control (MPC) according to intraoperative force feedback. The proposed system was thoroughly validated on a human head phantom targeting a permanent tooth to simulate a real clinical scenario, yielding an average root-mean-square (RMS) error (tooth shape after preparation) of 0.17 mm and an overall mean execution time of 347.77 s, achieving a 74.2% improvement in cutting efficiency over state-of-the-art methods. A comparative evaluation against conventional dental guides further demonstrates its technical feasibility and significant potential for clinical translation.