Autonomous Precision Milling of Biological Structures via Generic Anatomical Priors and Active Boundary Perception
Autonomous Precision Milling of Biological Structures via Generic Anatomical Priors and Active Boundary Perception
This research presents a state-adaptive autonomous milling framework that integrates generic anatomical priors with active boundary perception to address challenges in biological structure machining. The system uses semantic-guided registration and hybrid vision-force calibration to transform global guidance into robot-executable commands. When approaching uncertain boundaries, the robot actively probes structures to estimate boundary status and detachability based on stiffness changes. A state-adaptive controller manages transitions between perception and refinement cycles until termination criteria are met. Experiments on biological surrogates and in vivo mouse cranial window creation demonstrate accurate anatomical prior transfer, reliable boundary adaptation, and successful autonomous precision milling of biological structures.
The study demonstrates a state-adaptive control framework combining generic anatomical priors with active boundary perception to achieve autonomous precision milling on biological structures, validated through hierarchical experiments on surrogates and in vivo mouse cranial window creation.
Source: arXiv Robotics — research abstracts · Read original article ↗
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imaging limits scalability. This article presents a state-adaptive 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 approa
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o mouse cranial window creation demonstrate accurate anatomical prior transfer, reliable boundary adaptation, and autonomous precision milling of biological structures.
Open source S5
Source：arXiv Robotics — research abstracts · arxiv.org