Automating bone micro-milling using a robotic system presents challenges due to the uncertainties in both the external and internal features of bone tissue. For example, during mouse cranial window creation, a circular path with a radius of 2 to 4 mm needs to be milled on the mouse skull using a microdrill. The uneven surface and non-uniform thickness of the mouse skull make it difficult to fully automate this process, requiring the system to possess advanced perceptual and adaptive capabilities. In this study, we address this challenge by integrating a Microscopic Stereo Camera System (MSCS) into the robotic bone micro-milling system and proposing a novel online pre-measurement pipeline for the target surface. Starting from uncalibrated cameras, the pipeline enables automatic calibration and 3D surface fitting through a convolutional neural network (CNN)-based keypoint detection. Combined with the existing feedback-based system, we develop the world's first autonomous robotic bone micro-milling system capable of rapidly, in real-time perceiving and adapting to surface unevenness and non-uniform thickness, thereby enabling an end-to-end autonomous cranial window creation workflow without human assistance. Validation experiments on euthanized mice demonstrate that the improved system achieves a success rate of 85.7 % and an average milling time of 2.1 minutes, showing not only significant performance improvements over the previous system but also exceptional accuracy, speed, and stability compared to human operators.

翻译：利用机器人系统实现骨组织微铣削自动化面临挑战，主要源于骨组织外部与内部特征的不确定性。例如，在小鼠颅窗制备过程中，需要使用微型钻头在小鼠颅骨上铣削半径为2至4毫米的圆形路径。小鼠颅骨表面不平整且厚度不均匀的特性使得该过程难以完全自动化，要求系统具备先进的感知与自适应能力。本研究通过将显微立体相机系统（MSCS）集成至机器人骨组织微铣削系统，并提出针对目标表面的新型在线预测量流程来解决这一挑战。该流程从未经校准的相机出发，通过基于卷积神经网络（CNN）的关键点检测实现自动校准与三维表面拟合。结合现有基于反馈的系统，我们开发了世界上首个能够快速、实时感知并适应表面不平整性与厚度不均匀性的自主机器人骨组织微铣削系统，从而实现了无需人工干预的端到端自主颅窗制备工作流。在安乐死小鼠上进行的验证实验表明，改进后系统的成功率达到85.7%，平均铣削时间为2.1分钟，不仅较先前系统取得显著性能提升，相较于人工操作者也展现出卓越的精度、速度与稳定性。