Edge computing-driven scene-aware intelligent augmented reality assembly

Fu, Mingyu; Fang, Wei; Gao, Shan; Hong, Jian‐Hao

doi:10.1007/s00170-022-08758-4

Cited by 16 publications

(3 citation statements)

References 39 publications

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“…Nevertheless, the authors reveal low usability for longer periods of using the system with Microsoft HoloLens, leading to user errors. Fu et al present in [19] a scene-aware AR-based system, composed of a server for the image processing and a wearable Android device (i.e., AR glasses), which can recognize on the fly the assembly state and provide visual support for the operator. Deep learning was deployed with the YOLOv3 framework to recognize the manual assembly process.…”

Section: Related Work 21 Assembly Assistance Systemsmentioning

confidence: 99%

Real-Time Assembly Support System with Hidden Markov Model and Hybrid Extensions

et al. 2022

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This paper presents a context-aware adaptive assembly assistance system meant to support factory workers by embedding predictive capabilities. The research is focused on the predictor which suggests the next assembly step. Hidden Markov models are analyzed for this purpose. Several prediction methods have been previously evaluated and the prediction by partial matching, which was the most efficient, is considered in this work as a component of a hybrid model together with an optimally configured hidden Markov model. The experimental results show that the hidden Markov model is a viable choice to predict the next assembly step, whereas the hybrid predictor is even better, outperforming in some cases all the other models. Nevertheless, an assembly assistance system meant to support factory workers needs to embed multiple models to exhibit valuable predictive capabilities.

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Section: Related Work 21 Assembly Assistance Systemsmentioning

confidence: 99%

Real-Time Assembly Support System with Hidden Markov Model and Hybrid Extensions

et al. 2022

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“…This enables the overlay of virtual information onto real objects and automatic recognition of the assembly process and results. The primary goal is to reduce the skill requirements for workers, and improve assembly efficiency [9].…”

Section: Introductionmentioning

confidence: 99%

AR-Assisted Guidance for Assembly and Maintenance of Avionics Equipment

Xue,

Yang,

Chen

et al. 2024

Applied Sciences

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The assembly and maintenance of products in the aviation industry constitute a crucial aspect of the product life cycle, with numerous tasks still reliant on manual operations. In order to solve the problem of narrow operation spaces and blind areas in the processes of manual assembly and maintenance, we proposed an augmented reality (AR) assistant guidance method specifically designed for such scenarios. By employing a multi-modality anti-occlusion tracking algorithm, pose data of assembly parts can be obtained, upon which AR guidance information is displayed. Additionally, we proposed an assembly step identification method to alleviate user interaction pressure. We developed an AR visualization assistant guidance system and designed and conducted a user evaluation experiment to measure the learnability, usability, and mental effort required. The results demonstrate that our method significantly enhances training efficiency by 128.77%, as well as improving assembly and maintenance efficiency by 29.53% and 27.27% compared with traditional methods. Moreover, it has significant advantages in learnability, usability, and mental effort, providing a feasible and effective resolution for addressing blind areas during assembly and maintenance within the aviation industry.

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“…As a new human-computer interaction technology, AR has been used in different industrial scenarios, such as design, assembly and maintenance guidance (Mourtzis et al, 2019). This intuitive guidance leads to more efficient manual operations (Fu et al, 2022;Wang et al, 2016c).…”

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confidence: 99%

First- and Third-person Perspectives in the AR-assisted Blind Area Assembly

Feng

He²,

He³

et al. 2023

Preprint

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In the development of significant products, assembly efficiency is essential to the improvement of the output and quality of the equipment, and there is still a large amount of work that needs to be done manually by the user during the assembly process. Due to the complex internal structure of the product, the user’s hands, assembly parts, and internal construction are often invisible. The lack of visual information leads to low assembly efficiency, high error rates and user fatigue. In this study, we developed a video-assisted assembly guidance system, called ARScope, in the augmented reality (AR) environment. Users can freely place the mini camera module on the back of their hands or inside the narrow blind area and obtain the video from the firstperson perspective (FPP) and the third-person perspective (TPP). In AR, users can get a comfortable guiding experience by transforming, rotating and scaling the video screen. In addition, we designed and conducted the user study to investigate the effect of different viewpoints on assembly efficiency and user’s experience during video-assisted blind assembly in AR space, and also explored other influencing factors of this process. The results showed that FPP had more synergy with the user than TPP, resulting in an efficient assembly. In addition, the stability of the screen, the tactile feedback and the portability of the system hardware were also essential factors in the blind assembly process.

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Edge computing-driven scene-aware intelligent augmented reality assembly

Cited by 16 publications

References 39 publications

Real-Time Assembly Support System with Hidden Markov Model and Hybrid Extensions

Real-Time Assembly Support System with Hidden Markov Model and Hybrid Extensions

AR-Assisted Guidance for Assembly and Maintenance of Avionics Equipment

First- and Third-person Perspectives in the AR-assisted Blind Area Assembly

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