Autonomous Robotic Drilling System for Mice Cranial Window Creation

TL;DR

This paper presents an autonomous robotic system for creating cranial windows in mice, capable of adapting to biological variability without offline planning, and achieving a 70% success rate in experiments.

Contribution

The work introduces a novel autonomous robotic drilling system with real-time feedback and image-force based recognition, tailored for variable biological specimens.

Findings

Achieved 70% success rate in postmortem mice trials.

System outperforms existing deep learning methods in drilling completion recognition.

Average drilling time of 9.3 minutes per procedure.

Abstract

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.