mlsanityData to 14 Oct 2026

Autonomous Robotic Drilling System for Mice Cranial Window Creation

arXiv 2406.14135 ↗First posted 20 Jun 2024cs.RO cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 54 references that are in the map. First posted on arXiv 20 Jun 2024.

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19 of 54 references.

References · 19

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  2. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  3. 2017Mask R-CNNcited by 7,277
  4. 2015SSD: Single Shot MultiBox Detectorcited by 3,130
  5. 2015Learning Deconvolution Network for Semantic Segmentationcited by 365
  6. 2020BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentationcited by 294
  7. 2017A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Predictioncited by 245
  8. 2016UberNet: Training a `Universal' Convolutional Neural Network for Low-, Mid-, and High-Level Vision using Diverse Datasets and Limited Memorycited by 200
  9. 2017DSSD : Deconvolutional Single Shot Detectorcited by 131
  10. 2020Semantic Flow for Fast and Accurate Scene Parsingcited by 128
  11. 2015Learning to Diagnose with LSTM Recurrent Neural Networkscited by 99
  12. 2022PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllerscited by 84
7 more
  1. 2022PP-LiteSeg: A Superior Real-Time Semantic Segmentation Modelcited by 33
  2. 2017BlitzNet: A Real-Time Deep Network for Scene Understandingcited by 29
  3. 2015Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentationcited by 16
  4. 2020Real-time Semantic Segmentation with Context Aggregation Networkcited by 14
  5. 2019DQ Robotics: a Library for Robot Modeling and Controlcited by 3
  6. 2022A Multi-Arm Robotic Platform for Scientific Explorationcited by 1
  7. 2023Autonomous Robotic Drilling System for Mice Cranial Window Creation: An Evaluation with an Egg Modelcited by 1

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