Co2iAR:支持视听的同地移动协作式工业 AR 线束装配

IF 9.1 1区 计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Robotics and Computer-integrated Manufacturing Pub Date : 2024-06-03 DOI:10.1016/j.rcim.2024.102795
Wei Fang , Lixi Chen , Tienong Zhang , Hao Hu , Jiapeng Bi
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引用次数: 0

摘要

现有的增强现实(AR)装配主要是以第一人称视角为操作员提供视觉指示,很难为车间内的同地工人共享个人工作意图,特别是对于需要多名操作员协同工作的大型产品装配任务。为了在实际部署中弥补这一差距,本文提出了一种支持协同视听的移动协作式 AR 组装--Co2iAR。首先,根据立体视觉-惯性融合策略,为资源受限的移动 AR 平台实现了稳健、准确的自包含运动跟踪,然后由车间内的多个移动 AR 客户端进行共定位对齐。然后,提出了一种用于在线线束字符识别的轻量级文本感知网络,以及基于音频的确认策略,从而在共享的沉浸式工作场所内实现同地工人之间的自然视听交互,还可以监控当前的线束装配状态并自动激活分步教程。这项工作的新颖之处在于使用与部署同地协作式 AR 工作指示相同的设备来部署视听感知交互,从而在多个同地工人之间建立共享操作意图。最后,对多个 AR 客户端之间的协作性能进行了综合实验,结果表明所提出的 Co2iAR 可减轻认知负荷,在同地协作 AR 组装任务中实现卓越性能,提供更加以人为本的协作组装性能。
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Co2iAR: Co-located audio-visual enabled mobile collaborative industrial AR wiring harness assembly

Existing augmented reality (AR) assembly mainly provides visual instructions for operators from a first-person perspective, and it is hard to share individual working intents for co-located workers on the shop floor, especially for large-scale product assembly task that requires multiple operators working together. To bridge this gap for practical deployments, this paper proposes Co2iAR, a co-located audio-visual enabled mobile collaborative AR assembly. Firstly, according to the stereo visual-inertial fusion strategy, robust and accurate self-contained motion tracking is achieved for the resource-constrained mobile AR platform, followed by a co-located alignment from multiple mobile AR clients on the shop floor. Then, a lightweight text-aware network for online wiring harness character recognition is proposed, as well as the audio-based confirming strategy, enabling natural audio-visual interaction among co-located workers within a shared immersive workplace, which can also monitor the current wiring assembly status and activate the step-by-step tutorials automatically. The novelty of this work is focused on the deployment of audio-visual aware interaction using the same device that is being used to deploy the co-located collaborative AR work instructions, establishing shared operating intents among multiple co-located workers. Finally, comprehensive experiments are carried out on the collaborative performance among multiple AR clients, and results illustrate that the proposed Co2iAR can alleviate the cognitive load and achieve superior performance for the co-located AR assembly tasks, providing a more human-centric collaborative assembly performance.

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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
Knowledge extraction for additive manufacturing process via named entity recognition with LLMs Digital Twin-driven multi-scale characterization of machining quality: current status, challenges, and future perspectives A dual knowledge embedded hybrid model based on augmented data and improved loss function for tool wear monitoring A real-time collision avoidance method for redundant dual-arm robots in an open operational environment Less gets more attention: A novel human-centered MR remote collaboration assembly method with information recommendation and visual enhancement
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