Low-Code Programming of Spatial Contexts for Logistic Tasks in Mobile Robotics

Together with industrial partners, this project will investigate production scenarios where a machine can be operated by untrained personnel by using low-code development for adaptive and re-configurable robot programming of logistic tasks.

Embedded AI

AI currently relies on large data centers and centralized systems, necessitating data movement to algorithms. To address this limitation, AI is evolving towards a decentralized network of devices, bringing algorithms directly to the data. This shift, enabled by algorithmic agility and autonomous data discovery, will reduce the need for high-bandwidth connectivity and enhance data security and privacy, facilitating real-time edge learning.

Mobility Analytics using Sparse Mobility Data and Open Spatial Data

The amount of mobility-related data has increased massively which enables an increasingly wide range of analyses. When combined with digital representations of road networks and building interiors, this data holds the potential for enabling a more fine-grained understanding of mobility and for enabling more efficient, predictable, and environmentally friendly mobility.

Deep Learning and Automation of Image-Based Quality of Seeds and Grains

Today, manual visual inspection of grain is still one of the most important quality assurance procedures throughout the value chain of bringing cereals from the field to the table. 

Together with industrial partners, this project aims to develop and validate a method of automated imaging-based solutions that can replace subjective manual inspection and improve performance, robustness and consistency of the inspection. 

Edge-based AI Systems for Predictive Maintenance

Downtime of equipment is costly and a source of safety, security and legal issues. Today, organisations adopt a conservative schedule of preventive maintenance independent of the condition of equipment. This results in unnecessary service costs and occasional interruptions of production due to unexpected failures.