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 hence require intricate algorithms for successful autonomous robotic control. As a use case, we are studying the creation of cranial windows 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 mouse, sex, and age. In this work, we propose an autonomous robotic drilling method with no offline planning, consisting of a trajectory planning block with execution-time feedback with completion level recognition based on image and force information. The force information allows for completion-level resolution to increase 10 fold. We evaluate the proposed method in two ways. First, in an eggshell drilling task and achieved a success rate of 95% and average drilling time of 7.1 min out of 20 trials. Second, in postmortem mice and with a success rate of 70% and average drilling time of 9.3 min out of 20 trials.

翻译：在生命科学实验中引入机器人辅助操作有望实现优异的结果，而无需依赖科学家的操作技能。生命科学实验样本存在个体差异性，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手术为应用场景。该手术需要切除一块直径约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、性别和年龄等因素产生显著差异。本文提出一种无需离线规划的自主机器人钻孔方法，该方法包含基于图像与力信息反馈的轨迹规划模块，能够实时识别手术完成度。力信息的引入使完成度分辨率提升了十倍。我们通过两种方式评估所提方法：首先在蛋壳钻孔任务中进行了20次实验，成功率达95%，平均钻孔时间为7.1分钟；其次在小鼠尸体样本中进行了20次实验，成功率达70%，平均钻孔时间为9.3分钟。