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：生命科学实验操作中的机器人辅助有望实现对珍贵样本的精确操作，且不受科研人员技术水平限制。生命科学实验标本存在个体差异和形变，因此需要自主机器人控制。以小鼠颅窗安装为例，该操作需去除厚度约300微米的颅骨，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。颅骨厚度并不均匀，存在薄区和厚区，同时每次手术难以确保小鼠颅骨保持相同位置。测量所有这些特征并为每只小鼠预先编程机器人轨迹在现实中不可行。本文提出一种自主机器人钻孔方法，包括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，通过约束三次样条插值形成三维钻孔路径，同时避免过冲。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的任务完成度。由于鸡蛋在形状、厚度和力学特性上与小鼠颅骨具有相似特征，选择在不损伤蛋膜的前提下剥离蛋壳作为模拟任务。采用搭载钻头的6自由度机械臂评估该方法，在20次试验中成功率达80%。