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:实验操作的自主机器人控制在生命科学中得到了越来越广泛的应用,使得即使缺少操作技能的科学家也能够精确操纵珍贵的样本。生命科学中的实验样本因其个体差异和变形而需要自主机器人控制。以小鼠颅窗的制作为例。该操作需要剥离大约300微米厚的骨头,将其切割成直径为8毫米的圆形,但是小鼠的颅骨形状因品系、性别和年龄周数而异。颅骨的厚度不均匀,有些区域较薄,而其他区域则较厚。同时,保证小鼠的颅骨在每次手术时保持相同的位置也是非常困难的。因此,实现所有这些特征的测量并为每只小鼠预编程机器人轨迹是不现实的。因此,本文提出了一种自主钻孔方法。提出的方法包括钻孔轨迹规划和基于图像的任务完成水平识别。轨迹规划通过根据每个离散点的完成任务水平调整钻头的Z轴位置,并通过约束样条插值形成3D钻孔路径,同时避免超调。任务完成水平识别使用DSSD-Inspired深度学习模型来估计每个离散点的任务完成水平。由于鸡蛋在形状、厚度和动力学特性上与小鼠颅骨类似,因此选择去除蛋壳而不损伤下面的薄膜作为模拟任务。使用6-DOF机械臂持有钻头进行评估,成功率达到了20次试验中的80%。