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This paper details the development of a robotic system designed for autonomous full-crown tooth preparation, addressing the complexities of operating in densely constrained intraoral environments. The system features an anatomy-aware toolpath planning algorithm that adheres to a technician-designed preparation model while safeguarding adjacent teeth, paired with a clearance-oriented end-effector yaw assignment to minimize soft-tissue interference. Experimental results demonstrate the robot's capability to achieve an average geometric deviation of only 0.117 mm (RMSE), underscoring both the quality of restoration and clinical safety.
Achieving a mere 0.117 mm geometric deviation, this robotic system redefines precision in dental restorations within tightly constrained environments.
Tooth preparation refers to the controlled removal of tooth structure to create an optimal substrate for fixed restorations and is a core procedure in restorative dentistry. Automating this task is particularly challenging for robots because the dental bur must operate within a densely constrained intraoral workspace, where even sub-millimeter deviations can compromise outcomes or damage adjacent structures. This paper presents a novel robotic system for autonomous full-crown tooth preparation. The proposed framework includes: 1) an anatomy-aware toolpath planning algorithm that conforms precisely to a technician-designed preparation model while protecting adjacent teeth, and 2) a clearance-oriented end-effector yaw assignment strategy that allows intraoral access while reducing the risk of soft-tissue interference. Together, these features enable the robot to accurately mill the irregular tooth surface with an average geometric deviation of 0.117 mm (RMSE), achieving both restoration quality and clinical safety. A series of simulations and phantom-head experiments validate the system's feasibility and effectiveness.
Tsinghua AI