Robotic assistance for experimental manipulation in the life sciences is expected to enable precise manipulation of valuable samples, regardless of the skill of the scientist. Experimental specimens in the life sciences are subject to individual variability and deformation, and therefore require autonomous robotic control. As an example, we are studying the installation of a cranial window in a mouse. This operation requires the removal of the skull, which is approximately 300 um thick, to cut it into a circular shape 8 mm in diameter, but the shape of the mouse skull varies depending on the strain of mouse, sex and week of age. The thickness of the skull is not uniform, with some areas being thin and others thicker. It is also difficult to ensure that the skulls of the mice are kept in the same position for each operation. It is not realistically possible to measure all these features and pre-program a robotic trajectory for individual mice. The paper therefore proposes an autonomous robotic drilling method. The proposed method consists of drilling trajectory planning and image-based task completion level recognition. The trajectory planning adjusts the z-position of the drill according to the task completion level at each discrete point, and forms the 3D drilling path via constrained cubic spline interpolation while avoiding overshoot. The task completion level recognition uses a DSSD-inspired deep learning model to estimate the task completion level of each discrete point. Since an egg has similar characteristics to a mouse skull in terms of shape, thickness and mechanical properties, removing the egg shell without damaging the membrane underneath was chosen as the simulation task. The proposed method was evaluated using a 6-DOF robotic arm holding a drill and achieved a success rate of 80% out of 20 trials.

翻译：生命科学实验中的机器人辅助操作有望实现对珍贵样本的精确操作，不受操作者技能水平的影响。由于实验样本存在个体差异性和形变，需要自主机器人控制。以小鼠颅窗安装为例，该操作需移除约300微米厚的颅骨，将其切割为直径8毫米的圆形，但小鼠颅骨形状因品系、性别和周龄而异，且厚度不均，局部区域薄厚不一。同时，难以保证每次操作中小鼠颅骨处于相同位置。实际条件下无法测量所有特征并预先编程每只小鼠的机器人轨迹。为此，本文提出一种自主机器人钻孔方法，包含钻孔轨迹规划与基于图像的任务完成度识别。轨迹规划根据各离散点的任务完成度调整钻头Z轴位置，并通过约束三次样条插值生成三维钻孔路径以避免过冲。任务完成度识别采用基于DSSD的深度学习模型估计各离散点任务完成度。由于鸡蛋在形状、厚度与力学特性上与小鼠颅骨具有相似性，本研究选择在不损伤蛋膜前提下移除蛋壳作为模拟任务。通过搭载钻头的六自由度机械臂开展实验评估，该方法在20次试验中达到80%的成功率。