Human-Surgery-Inspired Robotic Craniotomy Framework: Multimodal Fusion and Adaptive Trajectory Adjustment

To address the problem that existing robotic craniotomy systems are mostly "open-loop" and cannot handle intraoperative registration errors and skull displacement, the researchers proposed a framework integrating multimodal perception and adaptive control, using dual-contour fusion trajectory planning with a spherical burr tool for single-stage spiral milling. Their CMA-TCN-Transformer network fuses force signals and milling sounds, then adaptive Bayesian filtering suppresses false triggers, and after penetration detection, the auxiliary trajectory is corrected in situ. Experiments on bovine ribs and ex vivo sheep skulls showed that the closed-loop system reduces feed rate to vsafe=1mm/s when progress ξ≥0.9.