Biological specimens exhibit significant variations in size and shape, challenging autonomous robotic manipulation. We focus on the mouse skull window creation task to illustrate these challenges. The study introduces a microscopic stereo camera system (MSCS) enhanced by the linear model for depth perception. Alongside this, a precise registration scheme is developed for the partially exposed mouse cranial surface, employing a CNN-based constrained and colorized registration strategy. These methods are integrated with the MSCS for robotic micromanipulation tasks. The MSCS demonstrated a high precision of 0.10 mm $\pm$ 0.02 mm measured in a step height experiment and real-time performance of 30 FPS in 3D reconstruction. The registration scheme proved its precision, with a translational error of 1.13 mm $\pm$ 0.31 mm and a rotational error of 3.38$^{\circ}$ $\pm$ 0.89$^{\circ}$ tested on 105 continuous frames with an average speed of 1.60 FPS. This study presents the application of a MSCS and a novel registration scheme in enhancing the precision and accuracy of robotic micromanipulation in scientific and surgical settings. The innovations presented here offer automation methodology in handling the challenges of microscopic manipulation, paving the way for more accurate, efficient, and less invasive procedures in various fields of microsurgery and scientific research.

翻译：生物样本在尺寸和形状上存在显著差异，这对自主机器人操作提出了挑战。本研究以小鼠颅骨开窗任务为例阐明这些挑战。该研究引入了一种通过线性模型增强深度感知的显微立体相机系统（MSCS）。与此同时，针对部分暴露的小鼠颅骨表面，开发了一种采用基于CNN的约束着色配准策略的精确配准方案。这些方法与MSCS集成，用于机器人微操作任务。MSCS在台阶高度实验中测量精度达到0.10 mm $\pm$ 0.02 mm，三维重建实时性能为30 FPS。配准方案在105个连续帧上测试（平均速度1.60 FPS）证明了其精度，平移误差为1.13 mm $\pm$ 0.31 mm，旋转误差为3.38$^{\circ}$ $\pm$ 0.89$^{\circ}$。本研究展示了MSCS及新型配准方案在提升科学和手术场景中机器人微操作精度与准确度方面的应用。本文提出的创新技术为应对显微操作挑战提供了自动化方法，为微外科和科学研究等多个领域实现更精准、高效、微创的手术操作铺平了道路。