自动化
机器人
注射器
计算机科学
移动机器人
任务(项目管理)
视觉伺服
人工智能
机器人学
模拟
人机交互
控制工程
系统工程
工程类
机械工程
作者
Angelos Angelopoulos,Matthew D. Verber,Collin McKinney,James F. Cahoon,Ron Alterovitz
标识
DOI:10.1109/iros55552.2023.10341743
摘要
Lab automation has the potential to accelerate scientific progress in the natural sciences, allowing tedious experiments that would require many hours of human time to be automated, enabling higher accuracy, efficiency, and repeatability. Mobile manipulation robots have the potential to work in chemistry labs designed for humans to complete tasks for which setting up customized factory-scale automation is premature or infeasible. We present a new method to enable a mobile manipulation robot to automate injections, a common task in chemistry labs when using equipment such as gas chromatographs (GCs) for analyzing the contents of a sample mixture. This task is challenging for a mobile manipulation robot due to the need to navigate to the equipment in the lab and then achieve millimeter-scale accuracy required for the syringe positioning. Our approach leverages deep learning to create a model capable of localizing the syringe with high accuracy using cameras mounted on the chemistry equipment, and then uses a visual servoing approach based on the syringe's needle localization to achieve the injection. We demonstrate that our approach is robust to uncertainty in navigation as well as uncertainty in the grasping position and orientation of the syringe, achieving errors sufficiently small to enable the mobile manipulation robot to automate injections in real chemistry equipment.
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