用开源框架实现低成本实验室机器人自动化，精准识别并操作微生物菌落。
An Open Source Computer Vision and Machine Learning Framework for Affordable Life Science Robotic Automation
- 用U-net分割菌落，结合混合密度网络预测机械臂关节角。
- 定位误差低于1mm，关节角预测误差小于4度，菌落检测IoU达0.537。
- 适合预算有限的生物实验室，可扩展至液体处理等任务。
我们提出一个开源机器人框架，融合计算机视觉与基于机器学习的逆运动学，实现低成本实验室自动化任务，如菌落挑选和液体处理。系统采用自训练U-net模型进行微生物培养物的语义分割，并结合混合密度网络预测简易5自由度机械臂的关节角度。通过改装机械臂并加装自定义液体处理末端执行器进行评估。实验结果表明，该框架可实现高精度、可重复的操作：平均位置误差低于1 mm，关节角预测误差低于4度；菌落检测的交并比（IoU）为0.537，骰子系数（Dice coefficient）为0.596，验证了其可行性。
原文摘要 · Abstract (English)
We present an open-source robotic framework that integrates computer vision and machine learning based inverse kinematics to enable low-cost laboratory automation tasks such as colony picking and liquid handling. The system uses a custom trained U-net model for semantic segmentation of microbial cultures, combined with Mixture Density Network for predicating joint angles of a simple 5-DOF robot arm. We evaluated the framework using a modified robot arm, upgraded with a custom liquid handling end-effector. Experimental results demonstrate the framework's feasibility for precise, repeatable operations, with mean positional error below 1 mm and joint angle prediction errors below 4 degrees and colony detection capabilities with IoU score of 0.537 and Dice coefficient of 0.596.
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