Laboratory processes involving small volumes of solutions and active ingredients are often performed manually due to challenges in automation, such as high initial costs, semi-structured environments and protocol variability. In this work, we develop a flexible and cost-effective approach to address this gap by introducing a vision-based system for liquid volume estimation and a simulation-driven pouring method particularly designed for containers with small openings. We evaluate both components individually, followed by an applied real-world integration of cell culture automation using a UR5 robotic arm. Our work is fully reproducible: we share our code at at \url{https://github.com/DaniSchober/LabLiquidVision} and the newly introduced dataset LabLiquidVolume is available at https://data.dtu.dk/articles/dataset/LabLiquidVision/25103102.
@article{arxiv.2404.16529,
title = {Vision-based robot manipulation of transparent liquid containers in a laboratory setting},
author = {Daniel Schober and Ronja Güldenring and James Love and Lazaros Nalpantidis},
journal= {arXiv preprint arXiv:2404.16529},
year = {2024}
}
Comments
This work has been submitted to the IEEE for possible publication