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Robotic assistance for experimental manipulation in the life sciences isexpected to enable precise manipulation of valuable samples, regardless of theskill of the scientist. Experimental specimens in the life sciences are subjectto individual variability and deformation, and therefore require autonomousrobotic control. As an example, we are studying the installation of a cranialwindow in a mouse. This operation requires the removal of the skull, which isapproximately 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, sexand week of age. The thickness of the skull is not uniform, with some areasbeing thin and others thicker. It is also difficult to ensure that the skullsof the mice are kept in the same position for each operation. It is notrealistically possible to measure all these features and pre-program a robotictrajectory for individual mice. The paper therefore proposes an autonomousrobotic drilling method. The proposed method consists of drilling trajectoryplanning and image-based task completion level recognition. The trajectoryplanning adjusts the z-position of the drill according to the task completionlevel at each discrete point, and forms the 3D drilling path via constrainedcubic spline interpolation while avoiding overshoot. The task completion levelrecognition uses a DSSD-inspired deep learning model to estimate the taskcompletion level of each discrete point. Since an egg has similarcharacteristics to a mouse skull in terms of shape, thickness and mechanicalproperties, removing the egg shell without damaging the membrane underneath waschosen as the simulation task. The proposed method was evaluated using a 6-DOFrobotic arm holding a drill and achieved a success rate of 80% out of 20trials.

Autonomous Robotic Drilling System for Mice Cranial Window Creation: An Evaluation with an Egg Model