Autonomous Precision Milling of Biological Structures via Generic Anatomical Priors and Active Boundary Perception

2026-09-11

AI Summary

本文提出一种结合通用解剖先验和主动边界感知的不确定性感知自主铣削框架，以解决生物结构精确铣削中目标几何、材料厚度及内部边界未知的问题。

Abstract

Autonomous precision milling of biological structures is challenged by incomplete knowledge of target geometry, local material thickness, and critical internal boundaries. Subject-specific preoperative models can address geometric and thickness variations, but static models cannot determine boundary status encountered during execution, while repeated target-specific imaging limits scalability. This article presents an uncertainty-aware autonomous milling framework that assigns complementary roles to generic anatomical priors and active boundary perception. A generic anatomical prior provides conservative global guidance and is transformed through semantic-guided registration and hybrid vision-force calibration into robot-executable guidance for individual targets. As milling approaches uncertain boundaries, the robot actively probes the remaining structure and uses relative stiffness changes to estimate boundary status and structural detachability. A state-adaptive controller governs transitions between active perception and spatially selective incremental refinement, repeating this cycle until the termination criterion is satisfied. Hierarchical experiments on biological surrogates and in vivo mouse cranial window creation demonstrate accurate anatomical prior transfer, reliable boundary adaptation, and autonomous precision milling of biological structures.

Problem

• autonomous precision milling
• biological structures
• incomplete knowledge
• target geometry
• local material thickness

Innovation

• uncertainty-aware autonomous milling
• generic anatomical priors
• active boundary perception
• semantic-guided registration
• hybrid vision-force calibration