  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.
