实现机器人实验室自动化即插即用:机器人集成参考架构模型。

IF 2.5 4区 医学 Q3 BIOCHEMICAL RESEARCH METHODS
Ádám Wolf , Panna Zsoldos , Károly Széll , Péter Galambos
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引用次数: 0

摘要

在生物医学研究与开发实验室中,辅助机器人解决方案从人类劳动力手中接过了平凡但必不可少的任务。协作机器人和移动机器人的最新技术可以在以人为本的低结构化环境中使用。然而,这些技术的适应性因其带来的额外复杂性而受到阻碍。在本文中,我们的目标是将错综复杂的实验室机器人集成架构联系起来。我们首先对实验室工作流程进行分层分解,并将活动表示映射到自动化控制架构的层和组件上。我们以拾取和放置实验室器皿的运输为例,详细阐述了这一框架--这是一个关键的辅助步骤,也是该领域专家最感兴趣的领域。我们的概念提案可作为参考架构模型,其主要原则已用于参考实施,并与国际标准化工作保持一致。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards robotic laboratory automation plug & play: Reference architecture model for robot integration

Supportive robotic solutions take over mundane, but essential tasks from human workforce in biomedical research and development laboratories. The newest technologies in collaborative and mobile robotics enable the utilization in the human-centered and low-structured environment. Their adaptability, however, is hindered by the additional complexity that they introduce. In our paper we aim to entangle the convoluted laboratory robot integration architectures. We begin by hierarchically decomposing the laboratory workflows, and mapping the activity representations to layers and components of the automation control architecture. We elaborate the framework in detail on the example of pick-and-place labware transportation - a crucial supportive step, which we identified as the number one area of interest among experts of the field. Our concept proposal serves as a reference architecture model, the key principles of which were used in reference implementations, and are in line with international standardization efforts.

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来源期刊
SLAS Technology
SLAS Technology Computer Science-Computer Science Applications
CiteScore
6.30
自引率
7.40%
发文量
47
审稿时长
106 days
期刊介绍: SLAS Technology emphasizes scientific and technical advances that enable and improve life sciences research and development; drug-delivery; diagnostics; biomedical and molecular imaging; and personalized and precision medicine. This includes high-throughput and other laboratory automation technologies; micro/nanotechnologies; analytical, separation and quantitative techniques; synthetic chemistry and biology; informatics (data analysis, statistics, bio, genomic and chemoinformatics); and more.
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