A systematic review of Augmented Reality interfaces for collaborative industrial robots

Pace, Francesco De; Manuri, Federico; Sanna, Andrea; Fornaro, C.

doi:10.1016/j.cie.2020.106806

Cited by 64 publications

(35 citation statements)

References 42 publications

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“…A systematic literature review (SLR) approach has been adopted to investigate the literature relevance of HCD and UX-based methodologies applied to HRI in collaborative tasks. Replicability and objectivity have been considered as basic principles in carrying out the research: the review follows the PICOC framework proposed by [18], thanks to its systematicity and completeness. PICOC [31] stands for a list of items to consider in the analysis, respectively population, intervention, comparison, outcomes and context.…”

Section: Methodology 21 Systematic Literature Reviewmentioning

confidence: 99%

“…Current AR applications are provided mainly through tablets since head-mounted display are still far from being industrially reliable, especially as for ergonomic aspects. The state of art in literature regarding AR-supported HRI highlighted different application areas: visualizing robot actions or faults to support troubleshooting [14]; controlling robots combining head and eye gaze; visual simultaneous localization and mapping algorithms [15]; understanding the impact of AR cues on human attention [16]; supporting human-robot collaborative assembly [17]; providing workspace and robot's volume monitoring [8,18]; improving interaction efficiency by reducing the physical strength (especially in heavy-duty industries) or letting older people to continue working in production facilities [19]; and by helping operators to have an immediate comprehension of the robot intentions in a quick and intuitive way (e.g., making visible the robot's planned motion and task state) [20,21] or adapting the AR contents to the specific environmental or task conditions [22,23].…”

Section: Introductionmentioning

confidence: 99%

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UX in AR-Supported Industrial Human–Robot Collaborative Tasks: A Systematic Review

et al. 2021

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The fourth industrial revolution is promoting the Operator 4.0 paradigm, originating from a renovated attention towards human factors, growingly involved in the design of modern, human-centered processes. New technologies, such as augmented reality or collaborative robotics are thus increasingly studied and progressively applied to solve the modern operators’ needs. Human-centered design approaches can help to identify user’s needs and functional requirements, solving usability issues, or reducing cognitive or physical stress. The paper reviews the recent literature on augmented reality-supported collaborative robotics from a human-centered perspective. To this end, the study analyzed 21 papers selected after a quality assessment procedure and remarks the poor adoption of user-centered approaches and methodologies to drive the development of human-centered augmented reality applications to promote an efficient collaboration between humans and robots. To remedy this deficiency, the paper ultimately proposes a structured framework driven by User eXperience approaches to design augmented reality interfaces by encompassing previous research works. Future developments are discussed, stimulating fruitful reflections and a decisive standardization process.

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Section: Methodology 21 Systematic Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

UX in AR-Supported Industrial Human–Robot Collaborative Tasks: A Systematic Review

et al. 2021

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“…Liu et al [57] came up with a bee algorithm for robotic assembly. Augmented reality (AR) has also been used to further increase the efficiency of COBOTs by [63,122]. This addition will see certain EoL products that would have normally been recycled being remanufactured and this will have a great impact on the environment as well.…”

Section: Collaborative Robots (Cobots)mentioning

confidence: 99%

A Review on the Lifecycle Strategies Enhancing Remanufacturing

2021

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Remanufacturing is a domain that has increasingly been exploited during recent years due to its numerous advantages and the increasing need for society to promote a circular economy leading to sustainability. Remanufacturing is one of the main end-of-life (EoL) options that can lead to a circular economy. There is therefore a strong need to prioritize this option over other available options at the end-of-life stage of a product because it is the only recovery option that maintains the same quality as that of a new product. This review focuses on the different lifecycle strategies that can help improve remanufacturing; in other words, the various strategies prior to, during or after the end-of-life of a product that can increase the chances of that product being remanufactured rather than being recycled or disposed of after its end-of-use. The emergence of the fourth industrial revolution, also known as industry 4.0 (I4.0), will help enhance data acquisition and sharing between different stages in the supply chain, as well boost smart remanufacturing techniques. This review examines how strategies like design for remanufacturing (DfRem), remaining useful life (RUL), product service system (PSS), closed-loop supply chain (CLSC), smart remanufacturing, EoL product collection and reverse logistics (RL) can enhance remanufacturing. We should bear in mind that not all products can be remanufactured, so other options are also considered. This review mainly focuses on products that can be remanufactured. For this review, we used 181 research papers from three databases; Science Direct, Web of Science and Scopus.

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“…What has preceded affirms that the benefits of combining AI and AR in robotics are manifold, and special attention should be given to such efforts. There are several review papers highlighting the integration of augmented reality to robotics from different perspectives such as human-robot interaction ( Green et al, 2008 ; Williams et al, 2018 ), industrial robotics ( De Pace et al, 2020 ), robotic-assisted surgery (L. Qian et al, 2020 ), and others ( Makhataeva and Varol, 2020 ). Similarly, there exist papers addressing the potential of integrating artificial intelligence in robotics as reviewed in Loh (2018) , De Pace et al (2020) and Tussyadiah (2020) .…”

Section: Introductionmentioning

confidence: 99%

“…There are several review papers highlighting the integration of augmented reality to robotics from different perspectives such as human-robot interaction ( Green et al, 2008 ; Williams et al, 2018 ), industrial robotics ( De Pace et al, 2020 ), robotic-assisted surgery (L. Qian et al, 2020 ), and others ( Makhataeva and Varol, 2020 ). Similarly, there exist papers addressing the potential of integrating artificial intelligence in robotics as reviewed in Loh (2018) , De Pace et al (2020) and Tussyadiah (2020) . A recent review ( Makhataeva and Varol, 2020 ) summarizes the work done at the intersection of AR and Robotics, yet it only mentions how augmented reality has been used within the context of robotics and does not touch on the intelligence in the system from different perspectives as highlighted in this paper.…”

Section: Introductionmentioning

confidence: 99%

Augmented Reality Meets Artificial Intelligence in Robotics: A Systematic Review

Bassyouni

Elhajj

2021

Front. Robot. AI

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Recently, advancements in computational machinery have facilitated the integration of artificial intelligence (AI) to almost every field and industry. This fast-paced development in AI and sensing technologies have stirred an evolution in the realm of robotics. Concurrently, augmented reality (AR) applications are providing solutions to a myriad of robotics applications, such as demystifying robot motion intent and supporting intuitive control and feedback. In this paper, research papers combining the potentials of AI and AR in robotics over the last decade are presented and systematically reviewed. Four sources for data collection were utilized: Google Scholar, Scopus database, the International Conference on Robotics and Automation 2020 proceedings, and the references and citations of all identified papers. A total of 29 papers were analyzed from two perspectives: a theme-based perspective showcasing the relation between AR and AI, and an application-based analysis highlighting how the robotics application was affected. These two sections are further categorized based on the type of robotics platform and the type of robotics application, respectively. We analyze the work done and highlight some of the prevailing limitations hindering the field. Results also explain how AR and AI can be combined to solve the model-mismatch paradigm by creating a closed feedback loop between the user and the robot. This forms a solid base for increasing the efficiency of the robotic application and enhancing the user’s situational awareness, safety, and acceptance of AI robots. Our findings affirm the promising future for robust integration of AR and AI in numerous robotic applications.

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A systematic review of Augmented Reality interfaces for collaborative industrial robots

Cited by 64 publications

References 42 publications

UX in AR-Supported Industrial Human–Robot Collaborative Tasks: A Systematic Review

UX in AR-Supported Industrial Human–Robot Collaborative Tasks: A Systematic Review

A Review on the Lifecycle Strategies Enhancing Remanufacturing

Augmented Reality Meets Artificial Intelligence in Robotics: A Systematic Review

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