Multimodal Interface for Human–Robot Collaboration

Human–robot collaboration (HRC) is one of the key aspects of Industry 4.0 (I4.0) and requires intuitive modalities for humans to communicate seamlessly with robots, such as speech, touch, or bodily gestures. However, utilizing these modalities is usually not enough to ensure a good user experience a...

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Bibliographic Details
Published in:Machines (Basel) 2022-10, Vol.10 (10), p.957
Main Authors: Rautiainen, Samu, Pantano, Matteo, Traganos, Konstantinos, Ahmadi, Seyedamir, Saenz, José, Mohammed, Wael M., Martinez Lastra, Jose L.
Format: Article
Language:English
Subjects:
Automation
Collaboration
Evaluation
Factories
Gesture recognition
hand gesture
Human factors
human–robot collaboration
human–robot interaction
Industrial applications
Industrial plants
Industry 4.0
Manufacturing
natural interaction
Robots
Sensors
User experience
User interfaces
Workload
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Summary:Human–robot collaboration (HRC) is one of the key aspects of Industry 4.0 (I4.0) and requires intuitive modalities for humans to communicate seamlessly with robots, such as speech, touch, or bodily gestures. However, utilizing these modalities is usually not enough to ensure a good user experience and a consideration of the human factors. Therefore, this paper presents a software component, Multi-Modal Offline and Online Programming (M2O2P), which considers such characteristics and establishes a communication channel with a robot with predefined yet configurable hand gestures. The solution was evaluated within a smart factory use case in the Smart Human Oriented Platform for Connected Factories (SHOP4CF) EU project. The evaluation focused on the effects of the gesture personalization on the perceived workload of the users using NASA-TLX and the usability of the component. The results of the study showed that the personalization of the gestures reduced the physical and mental workload and was preferred by the participants, while overall the workload of the tasks did not significantly differ. Furthermore, the high system usability scale (SUS) score of the application, with a mean of 79.25, indicates the overall usability of the component. Additionally, the gesture recognition accuracy of M2O2P was measured as 99.05%, which is similar to the results of state-of-the-art applications.
ISSN:2075-1702
2075-1702
DOI:10.3390/machines10100957