Abstract
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.
Abstract (translated)
生命科学中的实验操作需要机器人辅助,期望能够精确操纵宝贵的样本,无论科学家的技能如何。生命科学中的实验样本具有个体变量和变形,因此需要自主机器人控制。举个例子,我们正在研究在老鼠中安装颅骨窗口的操作。这需要将颅骨削减到大约300微米的厚度,以将其切成圆形形状直径为8毫米,但老鼠的颅骨形状因老鼠品种、性别和年龄而异。颅骨的厚度不是一致的,某些区域薄一些,某些区域则更厚。确保每只老鼠的颅骨在每只操作中都保持在相同的位置很困难。实际上,很难测量所有这些特征并为每只老鼠预先编程机器人路径。因此,本文提出了一种自主机器人钻头的方法。该方法包括钻头路径规划和图像任务完成水平识别。路径规划根据每个离散点的任务完成水平调整钻头的z位置,并通过约束立方曲线插值形成3D钻孔路径,同时避免过度延伸。任务完成水平识别使用DSSD启发式的深度学习模型来估计每个离散点的任务完成水平。因为鸡蛋的外壳形状、厚度和机械性质与老鼠颅骨具有类似的特点,因此选择不破坏外层膜来去除蛋壳作为模拟任务。该方法使用一个带有钻头的6自由度机器人手臂进行评价,在20次试验中取得了80%的成功率。
URL
https://arxiv.org/abs/2303.12265
