EVENFLOW Use Cases: Industry 4.0
A growing number of applications rely on AI-based solutions to carry-out mission-critical tasks, many of which are of temporal nature, dealing with ever-evolving flows of information. Crucial for mitigating threats and taking advantage of opportunities in such domains, is the ability to forecast imminent situations and critical complex events ahead of time. The EVENFLOW project works on developing hybrid learning techniques for complex event forecasting, which combine deep learning with logic-based learning and reasoning into neuro-symbolic forecasting models.
EVENFLOW conducts research work on three challenging use cases related to i) oncological forecasting in personalised medicine, ii) safe and efficient behaviour of autonomous transportation robots in smart factories and iii) reliable life cycle assessment of critical infrastructure.
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Autonomous robots are the future of factory automation, but they face big challenges navigating busy production floors. The European research project EVENFLOW aims to equip robots with advanced forecasting capabilities to avoid mishaps. The core innovation of EVENFLOW is developing methods to predict complex incidents before they occur. This could enable the preemptive avoidance of disasters, system failures and other problems across many domains. One application is making autonomous mobile robots (AMRs) in factories smarter and safer. Today’s AMRs rely on sensors to haul materials and connect workstations independently. However, their limited perception means they sometimes stall or collide with obstacles like other robots, objects or people – disrupting production.
The factory environment poses an additional challenge: massive streams of high-frequency sensor data. AMRs have constrained on-board computing to analyse this data flood in real time. EVENFLOW researchers are creating efficient predictive models that can distil the data and run on AMRs to forecast issues before they arise. By leveraging large datasets and simulations, EVENFLOW aims to model the complexity and give robots an intuition about when something is likely to go wrong. This technology could make AMRs much more reliable and safe, improving manufacturing productivity and quality.