Morphable Graph  is a generative, graph-based approach for data-driven motion modeling and synthesis. Motion capture data is represented by a directed graph and motion synthesis tasks are converted to graph searching problem.
INTERACT – Interactive Manual Assembly Operations for the Human-Centered Workplaces of the Future
In order to be competitive in a global scale, European factories should be operated by a highly skilled workforce supported by advanced automation and IT tools. The European research project INTERACT aims to utilize workers’ knowledge on executing manual assembly tasks and include it in the digital tools used to support design, verification, validation, modification and continuous improvement of human-centred, flexible assembly workplaces.
INVERSIV: Integrated Verification, Simulation and Visualization for Industrial Applications
Industry 4.0 is a main topic in the high-tech strategy of the German government, aimed at enabling fundamental innovation in industry. The idea behind Industry 4.0 is that “driven by the Internet, the real and virtual worlds are growing closer and closer together to form the Internet of Things. Industrial production of the future will be characterized by the strong individualization of products under the conditions of highly flexible (large series) production, the extensive integration of customers and business partners in business and value-added processes, and the linking of production and high-quality services leading to so-called hybrid products” (BMBF). Together with the increasing requirements of high flexibility, reduced delivery time, and short product life cycles, the Industry 4.0 concept represents the highly dynamic, individualized, and networked environment of modern, digital factories.
There is a large number of challenges on the IT side for realizing Industry 4.0: (i) The high flexibility of production processes requires the ability to quickly redesign and adapt production lines and all supporting processes in a company. (ii) The high variability of products with small batch sizes requires novel, highly adaptable ways to monitor the production line for quality and errors while providing support and training for workers that adapts to the current situation. (iii) To support quick changes we must move from fixed, specialized networks and interfaces to flexible architectures and service interfaces that can easily be reconfigured and support the low-latency, high-volume communication needed in industrial environments.
The main objective of the INVERSIV project is the ability to build fully functional models of systems (such as production lines) and from those models derive the data to monitor, predict, and possibly suggest corrections to the operation of those systems based on live data from real systems (dual reality).
INVERSIV aims at processing and using realtime data streams in production scenarios for visualizing the state of production facilities, detecting failures and problematic situations, and propose and visualize appropriate maintenance and repair actions. In case an error situation has been detected (respectively predicted) actions to resolve the situation have to be planned. We will explore the setup and evaluation of alternative models in terms of hybrid automata and verify their proper functionality with an extended hybrid verification system. The planning stage will also explore maintenance repair actions generated by involving human or intelligent virtual characters, e.g. for installing an alternative model. This highlights again the need to have common data representation and communication mechanism between the modules (here, multi-agent planning and hybrid verification).
The INVERSIV project is funded by the Federal Ministry of Education and Research (FKZ 01IW14004).
Ansprechpartner: Ingo Zinnikus