Robotic assistance for experimental manipulation in the life sciences is expected to enable favorable outcomes, regardless of the skill of the scientist. Experimental specimens in the life sciences are subject to individual variability and hence require intricate algorithms for successful autonomous robotic control. As a use case, we are studying the cranial window creation in mice. This operation requires the removal of an 8-mm circular patch of the skull, which is approximately 300 um thick, but the shape and thickness of the mouse skull significantly varies depending on the strain of the mouse, sex, and age. In this work, we develop an autonomous robotic drilling system with no offline planning, consisting of a trajectory planner with execution-time feedback with drilling completion level recognition based on image and force information. In the experiments, we first evaluate the image-and-force-based drilling completion level recognition by comparing it with other state-of-the-art deep learning image processing methods and conduct an ablation study in eggshell drilling to evaluate the impact of each module on system performance. Finally, the system performance is further evaluated in postmortem mice, achieving a success rate of 70% (14/20 trials) with an average drilling time of 9.3 min.

翻译：生命科学实验操作中的机器人辅助有望实现与科学家技能水平无关的有利结果。生命科学实验样本存在个体差异，因此需要复杂的算法来实现成功的自主机器人控制。本研究以小鼠颅骨开窗操作为应用案例。该手术需要移除一个直径8毫米、厚度约300微米的圆形颅骨片，但小鼠颅骨的形状和厚度会因品系、性别和年龄的不同而产生显著变化。本文开发了一种无需离线规划的自主机器人钻孔系统，该系统包含一个具有执行时反馈的轨迹规划器，以及基于图像和力信息识别的钻孔完成度判定模块。实验中，我们首先通过与其他前沿深度学习图像处理方法进行比较，评估了基于图像与力的钻孔完成度识别性能，并在蛋壳钻孔实验中通过消融研究评估了各模块对系统性能的影响。最终，在死后小鼠颅骨上进一步评估系统性能，取得了70%的成功率（20次尝试中成功14次），平均钻孔时间为9.3分钟。