A Flexible AR-based Training System for Industrial Maintenance

Sanna, Andrea; Manuri, Federico; Piumatti, Gîovanni; Paravati, Gianluca; Lamberti, Fabrizio; Pezzolla, Pietro

doi:10.1007/978-3-319-22888-4_23

Cited by 10 publications

(6 citation statements)

References 36 publications

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“…The application has been tested on a real maintenance case, which consists in a battery pack replacement of an industrial robot. Starting from an initial procedure cost of € 1,370 and a completion time of 9 h, the AR tool has reduced the overall cost to € 150 and the completion time of 2 h. Sanna et al ( 2015b ) proposed an AR maintenance collaborative system that allows a skilled remote operator to modify the virtual assets and instructions in real-time to give assistance to a local operator. The maintenance procedure is represented by a finite state machine composed by nodes and arcs.…”

Section: State Of Artmentioning

confidence: 99%

A Comparison Between Two Different Approaches for a Collaborative Mixed-Virtual Environment in Industrial Maintenance

et al. 2019

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Nowadays the market is becoming increasingly competitive, factories are required not only to enhance the product quality but also to reduce manufacturing and maintenance times. In an industrial context, modern factories are composed by many automated systems, such as industrial robots, which can perform different tasks. Although industrial robots are becoming more powerful and efficient, human workers are still required to accomplish different operations, such as training and maintenance procedures. The proposed research aims to assess a remote interaction system in an industrial training collaborative mixed-reality (CMR) environment. A remote expert user is capable of explaining a training procedure to an unskilled local user. Remote and local users interact using different interaction systems: the remote operator gives assistance using an immersive Virtual Reality (VR) device, whereas the local user interacts using a wearable Augmented Reality (AR) device. A comparison between an interaction based on the presence of a virtual human and one based on the use of abstract icons is proposed. In the first case, a virtual 3D representation of the remote technician is shown to the local user by using AR: the remote technician can pinpoint the components involved in the training procedure and the local user can visualize the instructions through some animations of the virtual avatar. In the second case, the local user cannot see a 3D representation of the remote technician; on the other hand, different 3D models, such as animated icons, are displayed to the local operator through AR depending on the component pinpointed by the remote technician in the virtual environment. Each 3D icon should suggest to the local user which component has to be manipulated at the current step of the procedure. Preliminary results suggest that the interface that requires less resources to be developed and managed should be preferred. Although in no audio condition the virtual avatar may improve the sense of presence of the remote technician, the use of abstract metaphors seems to be of primary importance to successfully complete an industrial task.

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Section: State Of Artmentioning

confidence: 99%

A Comparison Between Two Different Approaches for a Collaborative Mixed-Virtual Environment in Industrial Maintenance

et al. 2019

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“…Various industrial domains used AR technologies in training and supporting tasks, such as aerospace [23,38], automotive [39], and industrial plants [40]. A flexible training framework for maintenance was proposed by Sanna et al [25] to address the problem of creating AR content [41], which allowed instructors to generate maintenance procedures conveniently. Wang et al [28] described a remote collaborative for training in the manufacturing industry, enabling a remote expert to train a local worker in a physical assembly task.…”

Section: Maintenance Assembly Repair and Trainingmentioning

confidence: 99%

“…Moreover, AR applications can enable remote technicians to interactively support maintainers when AR aids are not sufficient. Training-closely associated with the maintenance, repair, and assembly tasks-is another application direction in the industry domain [23][24][25]. Instructors, teachers, and trainers tend to explore new methods to enhance learning efficiency and experience.…”

Section: Introductionmentioning

confidence: 99%

A Multi-User Collaborative AR System for Industrial Applications

Wang

2022

Sensors

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Augmented reality (AR) applications are increasingly being used in various fields (e.g., design, maintenance, assembly, repair, training, etc.), as AR techniques help improve efficiency and reduce costs. Moreover, collaborative AR systems extend applicability, allowing for collaborative environments for different roles. In this paper, we propose a multi-user collaborative AR system (aptly called the “multi-user collaborative system”, or MUCSys); it is composed of three ends—MUCStudio, MUCView, and MUCServer. MUCStudio aims to construct industrial content with CAD model transformation, simplification, database update, marker design, scene editing, and exportation, while MUCView contains sensor data analysis, real-time localization, scene loading, annotation editing, and virtual–real rendering. MUCServer—as the bridge between MUCStudio and MUCView—presents collaborative and database services. To achieve this, we implemented the algorithms of local map establishment, global map registration, optimization, and network synchronization. The system provides AR services for diverse industrial processes via three collaborative ways—remote support, collaborative annotation, and editing. According to the system, applications for cutting machines were presented to improve efficiency and reduce costs, covering cutting head designs, production line sales, and cutting machine inspections. Finally, a user study was performed to prove the usage experience of the system.

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“…The client side of the system was implemented by a library that manages the user interface and the communications with the server. The target application adopted in the considered case [18] was developed, using the Metaio SDK [62] AR framework. Specifically, it was built using the AREL technology, which lets the user create the interface as an HTML page with the logic defined in JavaScript.…”

Section: B Client Architecturementioning

confidence: 99%

“…The effectiveness of the framework was evaluated by using it in an ongoing investigation of the European Project EASE-R3 7 . The designed framework was exploited particularly to automatically generate a speech interface for a wearable-based AR application running on the Epson Moverio BT-200 8 smart glasses, used in maintenance and repair of machine tools [18]. The quality of the automatically-generated interface was assessed by comparing it with a user-generated interface, in terms of the impact of visual cues and semantic processing, and by evaluating the first-time user experience (FTUE).…”

Section: Introductionmentioning

confidence: 99%