实现以人为本的认知增强装配:视觉计算视角

IF 9.1 1区 计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Robotics and Computer-integrated Manufacturing Pub Date : 2024-08-22 DOI:10.1016/j.rcim.2024.102852
Jiazhen Pang, Pai Zheng, Junming Fan, Tianyuan Liu
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引用次数: 0

摘要

在工业 5.0 的背景下,以人为中心的装配正在成为实现大规模个性化的一种有前途的模式,因为它充分利用了机器人辅助下人的灵活性优势。然而,在小批量和高度定制化的装配任务中,生产程序的频繁变化给认知带来了巨大挑战。为解决这一问题,利用计算机视觉技术增强人类认知能力成为一种可行的解决方案。因此,本综述旨在探索人类的认知特点,并对现有计算机视觉技术进行分类,从而探讨认知增强型人本装配的未来发展。认知增强装配的概念首先是基于大脑的功能结构--额叶、顶叶、颞叶和枕叶而提出的。与这些脑区相对应,总结了空间性、记忆、知识和决策方面的认知问题。2014 年至 2023 年期间开展的有关装配视觉计算的最新研究分为四组:位置注册、多层识别、上下文感知和混合现实融合,这些研究都旨在解决这些认知挑战。报告还讨论了当前计算机视觉技术的应用和局限性。此外,考虑到元宇宙、云服务、大型语言模型和脑机接口等技术的快速发展,展望了计算机视觉的未来趋势,以增强与认知问题相对应的人类认知。
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Towards cognition-augmented human-centric assembly: A visual computation perspective

Human-centric assembly is emerging as a promising paradigm for achieving mass personalization in the context of Industry 5.0, as it fully capitalizes on the advantages of human flexibility with robot assistance. However, in small-batch and highly customized assembly tasks, frequently changes in production procedures pose significant cognition challenges. To address this, leveraging computer vision technology to enhance human cognition becomes a feasible solution. Therefore, this review aims to explore the cognitive characteristics of human beings and classify existing computer vision technologies in a manner that discusses the future development of cognition-augmented human-centric assembly. The concept of cognition-augmented assembly is first proposed based on the brain's functional structure - the frontal, parietal, temporal, and occipital lobes. Corresponding to these brain regions, cognitive issues in spatiality, memory, knowledge, and decision-making are summarized. Recent studies conducted between 2014 and 2023 on visual computation of assembly are categorized into four groups: position registration, multi-layer recognition, contextual perception, and mixed-reality fusion, all aimed at addressing these cognitive challenges. The applications and limitations of current computer vision technology are discussed. Furthermore, considering the rapidly evolving technologies such as the metaverse, cloud services, large language models, and brain-computer interfaces, future trends on computer vision are prospected to augment human cognition corresponding to the cognitive issues.

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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.
期刊最新文献
Editorial Board Efficient tool path planning method of ball-end milling for high quality manufacturing A safety posture field framework for mobile manipulators based on human–robot interaction trend and platform-arm coupling motion Processing accuracy improvement of robotic ball-end milling by simultaneously optimizing tool orientation and robotic redundancy Knowledge extraction for additive manufacturing process via named entity recognition with LLMs
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