Workshop on Challenges and Novel Approaches for Industry 4.0

The event was organized on premises of Switzerland Innovation Park Biel/Bienne. As this is a center of innovation, premises of a smart-factory and cobotics center, the symbolic meaning of this location resonated well with the event. The event started with keynote talks, addressing the topics of smart factory, cobotics, human-machine-interaction and general trends in the field of robotics and data-driven value-added services. After a networking lunch, an interactive, moderated design thinking workshop for identifying challenges and developing ideas and solutions was organized.

In form of impulse presentations. 5 speakers give inspiring insights into their research and application areas as well as highlighting current challenges to solve within their respective fields:

Prof. Dr Marc Pollefeys, Professor of Computer Science at ETH Zurich and the Director of the Microsoft Mixed Reality and AI Lab in Zurich, delivered a presentation on spatial computing and the industrial metaverse. He gave interesting examples how metaverse can be used for instructional trainings of workers at industrial settings and how spatial computing is contributing to more sophisticated mapping and localization of robots.

and employee acceptance and training/up-skilling must be ensured – all while considering the short- and long-term cost-benefit relations, ethical and moral issues, and cultural acceptance.

Cluster 2 refers to machine learning systems that react more flexibly/dynamically to process changes. On the one hand, regarding rapidly changing environmental conditions, on the other hand, related to highly dynamic process sequences (small batch manufacturing). This requires not only innovative approaches in human-machine interactions (intuitive, ease-of-use handling, no-code environments etc.) but also standardization in processes and interfaces as well as further developments in modular and self-learning ML systems. In this context, the challenge also arises as to how and whether the individual, experience-based knowledge of experts in a company can be transferred to (semi-)automated processes, e.g., the transformation of human intuition in process understanding to rule-based robot-supported systems.

Solutions Cluster 2:

• Development of automatization solutions that can meet the requirements of low volume/small batch production or highly variable process flows.
• Development of ML systems with improved flexibility in terms of self-learning/self-optimizing components so that they can better adapt to changing environments and high-complex processes.
• Development of monitoring systems to capture unconscious, intuitive human components in the manufacturing process and convert them into a rule-based, machine-executable program (e.g. ViT).

Solutions Cluster 3:

• Development of a tunnel scanning and cleaning system to identify and remove paint from vehicles – a combination of intelligent optical sensing for detection and characterization of paint and non-destructive automatization for cleaning/removal of paint while preserving the underlying paint/coating etc.
• Development of a tunnel scanning system to identify and characterize surface damages/deformations on large vehicles – a combination of intelligent passive and active optical sensing, resulting in a digital 3D-representation and classification of surface damages/deformations.
• System development of an automation solution for different tasks as a mobile implementation which works inside of large vehicles.
• Conceptual development of a holistic system (identifying segments, sub-processes, requirements) to support logistics/ergonomics, both in terms of the potential of autonomous (e.g., for, sorting, transport) and worker assistance systems (e.g., exoskeleton, human-robot collaborations).