AR-based interaction for human-robot collaborative manufacturing

Hietanen, Antti; Pieters, Roel; Lanz, Minna; Latokartano, Jyrki; Kämäräinen, Joni-Kristian

doi:10.1016/j.rcim.2019.101891

Cited by 147 publications

(59 citation statements)

References 39 publications

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“…This was a result of the ergonomic convenience during inspection, enhanced field of view, minimal crosstalk from the board while taking a reference and ability to collaborate. This result confirms the finding from [ 23 ] that projected that AR can be used effectively for displaying technical information with enhanced safety, ergonomics and autonomy over OST-HMD [ 49 ]. In the short-run, the difference was not significant, as the impact of wearables towards comfort was mitigated by the reduced experiment time.…”

Section: Discussionsupporting

confidence: 90%

Switchable Glass Enabled Contextualization for a Cyber-Physical Safe and Interactive Spatial Augmented Reality PCBA Manufacturing Inspection System

Runji

Lin

2020

Sensors

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Augmented reality (AR) has been demonstrated to improve efficiency by up to thrice the level of traditional methods. Specifically, the adoption of visual AR is performed widely using handheld and head-mount technologies. Despite spatial augmented reality (SAR) addressing several shortcomings of wearable AR, its potential is yet to be fully explored. To date, it enhances the cooperation of users with its wide field of view and supports hands-free mobile operation, yet it has remained a challenge to provide references without relying on restrictive static empty surfaces of the same object or nearby objects for projection. Towards this end, we propose a novel approach that contextualizes projected references in real-time and on demand, onto and through the surface across a wireless network. To demonstrate the effectiveness of the approach, we apply the method to the safe inspection of printed circuit board assembly (PCBA) wirelessly networked to a remote automatic optical inspection (AOI) system. A defect detected and localized by the AOI system is wirelessly remitted to the proposed remote inspection system for prompt guidance to the inspector by augmenting a rectangular bracket and a reference image. The rectangular bracket transmitted through the switchable glass aids defect localization over the PCBA, whereas the image is projected over the opaque cells of the switchable glass to provide reference to a user. The developed system is evaluated in a user study for its robustness, precision and performance. Results indicate that the resulting contextualization from variability in occlusion levels not only positively affect inspection performance but also supersedes the state of the art in user preference. Furthermore, it supports a variety of complex visualization needs including varied sizes, contrast, online or offline tracking, with a simple robust integration requiring no additional calibration for registration.

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Section: Discussionsupporting

confidence: 90%

Switchable Glass Enabled Contextualization for a Cyber-Physical Safe and Interactive Spatial Augmented Reality PCBA Manufacturing Inspection System

Runji

Lin

2020

Sensors

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“…• Over 50% of companies keep records of the robot reliability and safety data; Based on the literature analysis [30][31][32][33][34][35][36][37][38][39][40][41][42][43][44], the main human and robot factors influencing the production process that should be taken into account are presented in Table 1. In reference [1], the approximate efficiency of robotic application versus manual application was compared.…”

Section: Human and Robot Factors Influencing The Production Processmentioning

confidence: 99%

“…The transformation to a flexible and smart robotic system is considered as the next generation of manufacturing development in Industry 4.0 [29]. In some cases, the collaboration of human operators and robots can be possible [30][31][32]. Such a situation requires a very high safety level, but these cobot systems only comply with safety standards if they operate at a reduced speed [33].…”

Section: Introductionmentioning

confidence: 99%

Efficiency Analysis of Manufacturing Line with Industrial Robots and Human Operators

2020

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The problem of production flow and evaluation of productivity in the manufacturing line is analysed. Machines can be operated by humans or by robots. Since breakdowns and human factors affect the destabilization of the production processes, robots are preferred. The main problem is a proper methodology—how can we determine the real difference in work efficiency between human and robot at the design stage? Therefore, an analysis of the productivity and reliability of the machining line operated by human operators or industrial robots is presented. Some design variants and simulation models in FlexSim have been developed, taking into consideration the availability and reliability of the machines, operators and robots. Traditional productivity metrics, such as the throughput and utilization rate, are not very helpful for identifying the underlying problems and opportunities for productivity improvement in a manufacturing system, therefore we apply the OEE (overall equipment effectiveness) metric to present how the availability and reliability parameters influence the performance of the workstation, in the short and long terms. The implementation results of a real robotic line from industry are presented with the use of the overall factory efficiency (OFE) metric. The analysis may help factories achieve the level of world class manufacturing.

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“…This ensures the icon is always visible to the user's POV. In other HRC related studies, Hietanen et al [22] proposed using AR in HRC workspaces that shows robot status for safety purposes using projector or HMD (Hololens) with marker-based tracking. Similarly, Michalos et al [23], [24] and Makris et al [25] used AR in HRC systems to display robot trajectory through a HMD via marker-based tracking.…”

Section: Resultsmentioning

confidence: 99%

A Review on Augmented Reality Tracking Methods for Maintenance of Robots

et al. 2020

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Augmented reality (AR) in maintenance is a broad subject with many nuances when it comes to their implementation. The applications of these systems range from maintenance of large-scale assets such as buildings to smaller scale assets such as robots. Applications of AR in maintenance typically serves as a visual guide to assist users in diagnosis or steps needed to be performed for maintenance. In this paper, the tracking methods utilized in AR-based maintenance for robots are qualitatively evaluated. The reviewed works in this paper are between the years of 2015 to 2020 to ensure that the AR tracking methods are relatively state of the art. It is found that applications of AR-based maintenance for robots are uncommon in the scope defined in this research especially in the industrial environment as most reviewed works are conducted in a laboratory setting. In addition to that, it is found that marker-based tracking methods are commonly utilized in these applications.

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AR-based interaction for human-robot collaborative manufacturing

Cited by 147 publications

References 39 publications

Switchable Glass Enabled Contextualization for a Cyber-Physical Safe and Interactive Spatial Augmented Reality PCBA Manufacturing Inspection System

Switchable Glass Enabled Contextualization for a Cyber-Physical Safe and Interactive Spatial Augmented Reality PCBA Manufacturing Inspection System

Efficiency Analysis of Manufacturing Line with Industrial Robots and Human Operators

A Review on Augmented Reality Tracking Methods for Maintenance of Robots

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