Gaze-based Human Factors Measurements for the Evaluation of Intuitive Human-Robot Collaboration in Real-time

“…Vogel et al [ 95 ], Paletta et al [ 103 ], Kyjanek et al [ 104 ] and Chan et al [ 88 ] made use of Kuka collaborative robots. Vogel et al [ 95 ] uses a Kuka iiwa LBR 14 for assisting the operator to screw mounting plates to a ground plate in a cooperative assembly task, while Paletta et al [ 103 ] uses a Kuka iiwa LBR 7 on a toy problem, where the cobot assists the operator on a pick and place task, where the goal is to assemble a tangram puzzle (toy problem). Kyjanek et al [ 104 ] employs a Kuka cobot to support the human operator on a non-standard wooden construction system, where the cobot position and holds the part in the correct location while the operator attaches them.…”

Section: Systematic Literature Review Analysismentioning

confidence: 99%

“…Liu and Wang [ 94 ] uses a depth sensor for monitoring the distance between the robot and the operator, but no AR cues are displayed to the user. Paletta et al [ 103 ] tracks the user head and hands with a motion capturing system and computes the distance to the nearest robot part; based on that and on the participant’s stress level, the robot arm’s speed is re-adjusted downwards to a potential full stop. In addition, Dimitropoulos et al [ 114 ] uses the vibration feature of a smartwatch as cues for safety alerts.…”

Section: Systematic Literature Review Analysismentioning

confidence: 99%

“…On a more human–centered approach, the AR application in Paletta et al [ 103 ] projects into the user’s field of view, his current mental load, and concentration level, through a virtual yellow and orange bar, respectively. It also shows the users’ gaze, represented by a small blue sphere.…”

Section: Systematic Literature Review Analysismentioning

confidence: 99%

“…Paletta et al [ 103 ] and Chan et al [ 88 ] make usage of diagnostic and active gaze inputs, respectively. This means that, on the first work, eye movement and gaze tracking were used for assessing the operator’s concentration level, and task switch rate, while on the second work, the user could interact with the holograms or give input to the system by staring at some point of interest or blinking.…”

Section: Systematic Literature Review Analysismentioning

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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Attention Analysis for Assistance in Assembly Processes

Paletta¹

2021

IFIP Advances in Information and Communication Technology

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Human attention processes play a major role in the optimization of human-machine interaction (HMI) systems. This work describes a suite of innovative components within a novel framework in order to assess the human factors state of the human operator primarily by gaze and in real-time. The objective is to derive parameters that determine information about situation awareness of the human collaborator that represents a central concept in the evaluation of interaction strategies in collaboration. The human control of attention provides measures of executive functions that enable to characterize key features in the domain of human-machine collaboration. This work presents a suite of human factors analysis components (the Human Factors Toolbox) and its application in the assembly processes of a future production line. Comprehensive experiments on HMI are described which were conducted with typical tasks including collaborative pick-and-place in a lab based prototypical manufacturing environment.

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Gaze-based Human Factors Measurements for the Evaluation of Intuitive Human-Robot Collaboration in Real-time

Cited by 11 publications

References 13 publications

Editorial: Active Vision and Perception in Human-Robot Collaboration

Editorial: Active Vision and Perception in Human-Robot Collaboration

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

Attention Analysis for Assistance in Assembly Processes

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