人机协作中的装配复杂性和生理反应:初步实验分析的启示

IF 9.1 1区 计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Matteo Capponi, Riccardo Gervasi, Luca Mastrogiacomo, Fiorenzo Franceschini
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引用次数: 0

摘要

工业 5.0 范式重新激发了人们对人类领域的兴趣,强调了工人福祉在生产活动中的重要性。在此背景下,协作机器人技术应运而生,成为支持人类完成疲劳和重复性任务的一项技术。本研究利用认知努力的生理指标,研究了人机协作中装配复杂性的影响。在一系列实验中,参与者以两种方式完成了不同产品的不同复杂度的装配过程:手动和在机器人协助下。实验收集了包括皮肤电导率、心率变异性和眼动跟踪指标在内的生理指标。对生理信号的分析表明,装配复杂性和 cobot 支持会产生影响。这项研究的一个重要发现是,单一的生理信号通常无法提供对认知负荷的全面了解。因此,应采用综合方法。这种方法强调了同时考虑多种措施以准确评估工业环境中工人健康状况的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assembly complexity and physiological response in human-robot collaboration: Insights from a preliminary experimental analysis

Industry 5.0 paradigm has renewed interest in the human sphere, emphasizing the importance of workers’ well-being in manufacturing activities. In such context, collaborative robotics originated as a technology to support humans in tiring and repetitive tasks. This study investigates the effects of assembly complexity in Human-Robot collaboration using physiological indicators of cognitive effort. In a series of experiments, participants performed assembly processes of different products with varying complexity, in two modalities: manually and with cobot assistance. Physiological measures, including skin conductance, heart rate variability and eye-tracking metrics were collected. The analysis of physiological signals showed trends suggesting the impact of assembly complexity and cobot support. One key finding of the study is that a single physiological signal usually may not provide a complete understanding of cognitive load. Therefore, a holistic approach should be followed. This approach highlighted the importance of considering multiple measures simultaneously to accurately assess workers’ well-being in industrial environments.

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来源期刊
Robotics and Computer-integrated Manufacturing
Robotics and Computer-integrated Manufacturing 工程技术-工程:制造
CiteScore
24.10
自引率
13.50%
发文量
160
审稿时长
50 days
期刊介绍: The journal, Robotics and Computer-Integrated Manufacturing, focuses on sharing research applications that contribute to the development of new or enhanced robotics, manufacturing technologies, and innovative manufacturing strategies that are relevant to industry. Papers that combine theory and experimental validation are preferred, while review papers on current robotics and manufacturing issues are also considered. However, papers on traditional machining processes, modeling and simulation, supply chain management, and resource optimization are generally not within the scope of the journal, as there are more appropriate journals for these topics. Similarly, papers that are overly theoretical or mathematical will be directed to other suitable journals. The journal welcomes original papers in areas such as industrial robotics, human-robot collaboration in manufacturing, cloud-based manufacturing, cyber-physical production systems, big data analytics in manufacturing, smart mechatronics, machine learning, adaptive and sustainable manufacturing, and other fields involving unique manufacturing technologies.
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