An Augmented Reality Human-Robot Physical Collaboration Interface Design for Shared, Large-Scale, Labour-Intensive Manufacturing Tasks

Chan, Wesley P.; Hanks, Geoffrey; Sakr, Maram; Tiger, Zuo,; Croft, Elizabeth A.

doi:10.1109/iros45743.2020.9341119

Cited by 15 publications

(21 citation statements)

References 23 publications

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“…Despite that, according to Fernández del Amo et al [ 78 ], the quality assessment is a process prone to bias itself; therefore, the mean and standard deviation for each criterion is provided as a tool for numerical validation of a potential author bias in the assessment process. Not considering the quantitative criteria (QC1 and QC2), the papers that were perceived with the subjectively higher quality were Kalpagam et al [ 49 ] and Chan et al [ 88 ], followed by Materna et al [ 89 ], Hietanen et al [ 51 ], and Tsamis et al [ 90 ], with a score of 7.5 out of 8 for the first two, and 7 out of 8 for the following three, respectively. If one considers the quantitative criteria, the order of relevance changes to Hietanen et al [ 51 ], Kalpagam et al [ 49 ], Chan et al [ 88 ], Materna et al [ 89 ], and Michalos et al [ 91 ] figuring out in the last place instead of Hietanen et al [ 51 ] on the top 5, for a small score difference of 0.025 points.…”

Section: Methodsmentioning

confidence: 99%

“…Not considering the quantitative criteria (QC1 and QC2), the papers that were perceived with the subjectively higher quality were Kalpagam et al [ 49 ] and Chan et al [ 88 ], followed by Materna et al [ 89 ], Hietanen et al [ 51 ], and Tsamis et al [ 90 ], with a score of 7.5 out of 8 for the first two, and 7 out of 8 for the following three, respectively. If one considers the quantitative criteria, the order of relevance changes to Hietanen et al [ 51 ], Kalpagam et al [ 49 ], Chan et al [ 88 ], Materna et al [ 89 ], and Michalos et al [ 91 ] figuring out in the last place instead of Hietanen et al [ 51 ] on the top 5, for a small score difference of 0.025 points. This last place change may or may not revert, due to the quantitative criteria, as well as other papers’ classifications, over time, since newer papers will eventually be more cited in future works.…”

Section: Methodsmentioning

confidence: 99%

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Augmented Reality for Human–Robot Collaboration and Cooperation in Industrial Applications: A Systematic Literature Review

Costa

Petry

Moreira

2022

Sensors

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With the continuously growing usage of collaborative robots in industry, the need for achieving a seamless human–robot interaction has also increased, considering that it is a key factor towards reaching a more flexible, effective, and efficient production line. As a prominent and prospective tool to support the human operator to understand and interact with robots, Augmented Reality (AR) has been employed in numerous human–robot collaborative and cooperative industrial applications. Therefore, this systematic literature review critically appraises 32 papers’ published between 2016 and 2021 to identify the main employed AR technologies, outline the current state of the art of augmented reality for human–robot collaboration and cooperation, and point out future developments for this research field. Results suggest that this is still an expanding research field, especially with the advent of recent advancements regarding head-mounted displays (HMDs). Moreover, projector-based and HMDs developed approaches are showing promising positive influences over operator-related aspects such as performance, task awareness, and safety feeling, even though HMDs need further maturation in ergonomic aspects. Further research should focus on large-scale assessment of the proposed solutions in industrial environments, involving the solution’s target audience, and on establishing standards and guidelines for developing AR assistance systems.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Augmented Reality for Human–Robot Collaboration and Cooperation in Industrial Applications: A Systematic Literature Review

Costa

Petry

Moreira

2022

Sensors

View full text Add to dashboard Cite

show abstract

“…In De Pace et al [18], a series of interesting UI studies resembling HCD approaches were collected concerning whether exocentric or egocentric interfaces are the best in limit-ing the level of mental and physical involvement in controlling the manipulator. Another study by Chan et al [50] reconsidered AR-based interfaces for human-robot collaboration on large-scale labor-intensive manufacturing tasks (carbon-fiber-reinforced-polymer production) where the accent is on the perceived workload and efficiency. Indeed, as stated in other studies [18], the lowest physical and temporal demand is registered with appropriately designed AR solutions, reducing user's effort and sense of frustration while cutting down operational time.…”

Section: What Are the Main Benefits Of Adopting Ux Approaches In Designing Ar-supported Collaborative Solutions?mentioning

confidence: 99%

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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“…However, these approaches have not had much uptake. More recently, researchers have explored the benefits of using AR based interfaces to program robots by defining lowlevel inputs, such as trajectory points [2], [24]. This process was improved by [25], [26] to only require start and goal positions, allowing the robot to plan around obstacles.…”

Section: Robot Programming With Armentioning

confidence: 99%

“…Robotic object manipulation is one of the most ubiquitous tasks throughout robotics [1], with most works focusing on manipulating rigid objects. For robotic arms to be useful for real-world tasks, they also need to be able to manipulate deformable objects, for tasks ranging from carbon fibre layup [2], suturing [3], to household chores [4], [5] and cloth manipulation [6]. One such task, robotic cloth folding, is of particular interest to robotics community because of its high applicability.…”

Section: Introductionmentioning

confidence: 99%

Demonstrating Cloth Folding to Robots: Design and Evaluation of a 2D and a 3D User Interface

Waymouth¹,

Cosgun²,

Newbury³

et al. 2021

Preprint

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An appropriate user interface to collect human demonstration data for deformable object manipulation has been mostly overlooked in the literature. We present an interaction design for demonstrating cloth folding to robots. Users choose pick and place points on the cloth and can preview a visualization of a simulated cloth before real-robot execution. Two interfaces are proposed: A 2D display-and-mouse interface where points are placed by clicking on an image of the cloth, and a 3D Augmented Reality interface where the chosen points are placed by hand gestures. We conduct a user study with 18 participants, in which each user completed two sequential folds to achieve a cloth goal shape. Results show that while both interfaces were acceptable, the 3D interface was found to be more suitable for understanding the task, and the 2D interface suitable for repetition. Results also found that fold previews improve three key metrics: task efficiency, the ability to predict the final shape of the cloth and overall user satisfaction.

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An Augmented Reality Human-Robot Physical Collaboration Interface Design for Shared, Large-Scale, Labour-Intensive Manufacturing Tasks

Cited by 15 publications

References 23 publications

Augmented Reality for Human–Robot Collaboration and Cooperation in Industrial Applications: A Systematic Literature Review

Augmented Reality for Human–Robot Collaboration and Cooperation in Industrial Applications: A Systematic Literature Review

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

Demonstrating Cloth Folding to Robots: Design and Evaluation of a 2D and a 3D User Interface

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