arXiv subjects

Jianxia Hou

Publications and source records attributed to Jianxia Hou.

2 recordsLinked to original sources

arXiv2026

Coverage Planning for Robotic Tooth Preparation in Densely 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.

cs.RO↗

arXiv2026

From swept contact to pose: Probe-aware registration via complementary-shape docking

Accurate registration between a prior model and the real scene is essential for high-precision robotic manipulation, yet optical methods suffer from long calibration chains, line-of-sight constraints, and fabrication errors. We propose a calibration-free alternative that reformulates contact registration as complementary-shape docking between the object and the probe's swept volume, explicitly accounting for probe geometry and leveraging both contact and non-contact evidence. Our solver integrates a global-to-local search via 3D FFT correlation over low-discrepancy SO(3) samples, then followed by continuous SE(3) refinement using Lie-algebra updates and analytic contact sensitivities. This pipeline yields efficient exploration and metric-grade convergence without fragile point correspondences. Simulation across free-form meshes achieved sub-0.04 mm and sub-0.4{\deg} accuracy and robustness to pose noise and contact loss. On a tooth-preparation robot, our method attained 0.42 mm and 3.75{\deg}, outperforming an optical tracker registration while requiring no external sensors. These results demonstrate a practical and precise registration strategy for surgical and industrial robots.

cs.RO↗