Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen


Gursch Heimo, Silva Nelson, Reiterer Bernhard , Paletta Lucas , Bernauer Patrick, Fuchs Martin, Veas Eduardo Enrique, Kern Roman

Flexible Scheduling for Human Robot Collaboration in Intralogistics Teams

Mensch und Computer 2018, Gesellschaft für Informatik e.V., Gesellschaft für Informatik e.V., Bonn, Germany, 2018

The project Flexible Intralogistics for Future Factories (FlexIFF) investigates human-robot collaboration in intralogistics teams in the manufacturing industry, which form a cyber-physical system consisting of human workers, mobile manipulators, manufacturing machinery, and manufacturing information systems. The workers use Virtual Reality (VR) and Augmented Reality (AR) devices to interact with the robots and machinery. The right information at the right time is key for making this collaboration successful. Hence, task scheduling for mobile manipulators and human workers must be closely linked with the enterprise’s information systems, offering all actors on the shop floor a common view of the current manufacturing status. FlexIFF will provide useful, well-tested, and sophisticated solutions for cyberphysicals systems in intralogistics, with humans and robots making the most of their strengths, working collaboratively and helping each other.

Silva Nelson, Schreck Tobias, Veas Eduardo Enrique, Sabol Vedran, Eggeling Eva, Fellner Dieter W.

Leveraging Eye-gaze and Time-series Features to Predict User Interests and Build a Recommendation Model for Visual Analysis

ACM Symposium on Eye Tracking Research and Applications ETRA, ACM, 2018

We developed a new concept to improve the efficiency of visual analysis through visual recommendations. It uses a novel eye-gaze based recommendation model that aids users in identifying interesting time-series patterns. Our model combines time-series features and eye-gaze interests, captured via an eye-tracker. Mouse selections are also considered. The system provides an overlay visualization with recommended patterns, and an eye-history graph, that supports the users in the data exploration process. We conducted an experiment with 5 tasks where 30 participants explored sensor data of a wind turbine. This work presents results on pre-attentive features, and discusses the precision/recall of our model in comparison to final selections made by users. Our model helps users to efficiently identify interesting time-series patterns.
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