基于通用解剖先验与主动边界感知的生物结构自主精密铣削

中文摘要 AI 辅助

生物结构的自主精密铣削面临目标几何信息不完整、局部材料厚度未知以及关键内部边界不确定等挑战。针对特定个体的术前模型可以解决几何和厚度变异问题，但静态模型无法确定执行过程中遇到的边界状态，而重复的针对目标的成像则限制了可扩展性。本文提出了一种不确定性感知的自主铣削框架，该框架为通用解剖先验和主动边界感知分配互补角色。通用解剖先验提供保守的全局指导，并通过语义引导配准和混合视觉-力校准转化为针对个体目标的可执行机器人指导。当铣削接近不确定边界时，机器人主动探测剩余结构，并利用相对刚度变化来估计边界状态和结构可分离性。状态自适应控制器管理主动感知与空间选择性增量细化之间的转换，重复此循环直至满足终止标准。在生物替代物和体内小鼠颅窗创建上的分层实验证明了准确的解剖先验转移、可靠的边界适应以及生物结构的自主精密铣削。

英文摘要

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.

发表机构

• The University of Tokyo（东京大学）
• Tsinghua University（清华大学）

机构由 AI 辅助整理，请以论文原文为准。

补充信息

备注

18 pages, 12 figures. Submitted to IEEE Transactions on Robotics (T-RO) for possible publication