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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.

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.

The Scatter Plot Matrix (SPLOM) is a well-known technique for visual analysis of high-dimensional data. However, one problem of large SPLOMs is that typically not all views are potentially relevant to a given analysis task or user. The matrix itself may contain structured patterns across the dimensions, which could interfere with the investigation for unexplored views. We introduce a new concept and prototype implementation for an interactive recommender system supporting the exploration of large SPLOMs based on indirectly obtained user feedback from user eye tracking. Our system records the patterns that are currently under exploration based on gaze times, recommending areas of the SPLOM containing potentially new, unseen patterns for successive exploration. We use an image-based dissimilarity measure to recommend patterns that are visually dissimilar to previously seen ones, to guide the exploration in large SPLOMs. The dynamic exploration process is visualized by an analysis provenance heatmap, which captures the duration on explored and recommended SPLOM areas. We demonstrate our exploration process by a user experiment, showing the indirectly controlled recommender system achieves higher pattern recall as compared to fully interactive navigation using mouse operations.

Value stream mapping is a lean management method for analyzing and optimizing a series of events for production or services. Even today the first step in value stream analysis - the acquisition of the current state - is still created using pen & paper by physically visiting the production place. We capture a digital representation of how manufacturing processes look like in reality. The manufacturing processes can be represented and efficiently analyzed for future production planning by using a meta description together with a dependency graph. With our Value Stream Creator and explOrer (VASCO) we present a tool, which contributes to all parts of value stream analysis - from data acquisition, over planning, comparison with previous realities, up to simulation of future possible states.

Effective visual exploration of large data sets is an important problem. A standard tech- nique for mapping large data sets is to use hierarchical data representations (trees, or dendrograms) that users may navigate. If the data sets get large, so do the hierar- chies, and effective methods for the naviga- tion are required. Traditionally, users navi- gate visual representations using desktop in- teraction modalities, including mouse interac- tion. Motivated by recent availability of low- cost eye-tracker systems, we investigate ap- plication possibilities to use eye-tracking for controlling the visual-interactive data explo- ration process. We implemented a proof-of- concept system for visual exploration of hier- archic data, exemplified by scatter plot dia- grams which are to be explored for grouping and similarity relationships. The exploration includes usage of degree-of-interest based dis- tortion controlled by user attention read from eye-movement behavior. We present the basic elements of our system, and give an illustra- tive use case discussion, outlining the applica- tion possibilities. We also identify interesting future developments based on the given data views and captured eye-tracking information. (13) Visual Exploration of Hierarchical Data Using Degree-of-Interest Controlled by Eye-Tracking. Available from: https://www.researchgate.net/publication/309479681_Visual_Exploration_of_Hierarchical_Data_Using_Degree-of-Interest_Controlled_by_Eye-Tracking [accessed Oct 3, 2017].

Value stream mapping is a lean management method for analyzing and optimizing a series of events for production or services. Even today the first step in value stream analysis – the acquisition of the current state map – is still created using pen & paper by physically visiting the production line. We capture a digital representation of how manufacturing processes look like in reality. The manufacturing processes can be represented and efficiently analyzed for future production planning as a future state map by using a meta description together with a dependency graph. With VASCO we present a tool, which contributes to all parts of value stream analysis - from data acquisition, over analyzing, planning, comparison up to simulation of alternative future state maps.We call this a holistic approach for Value stream mapping including detailed analysis of lead time, productivity, space, distance, material disposal, energy and carbon dioxide equivalents – depending in a change of calculated direct product costs.

We present a new open-source prototype framework to exploreand visualize eye-tracking experiments data. Firstly, standard eyetrackersare used to record raw eye gaze data-points on user experiments.Secondly, the analyst can configure gaze analysis parameters,such as, the definition of areas of interest, multiple thresholdsor the labeling of special areas, and we upload the data to a searchserver. Thirdly, a faceted web interface for exploring and visualizingthe users’ eye gaze on a large number of areas of interest isavailable. Our framework integrates several common visualizationsand it also includes new combined representations like an eye analysisoverview and a clustered matrix that shows the attention timestrength between multiple areas of interest. The framework can bereadily used for the exploration of eye tracking experiments data.We make available the source code of our prototype framework foreye-tracking data analysis.