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.

Multimodal Data Processing of Earth Observation Data

Based on observations of the Earth, a range of Danish public organizations build and maintain important data foundations that are used for decision-making, e.g., for executing environmental law or making planning decisions in both private and public organizations in Denmark. This project aims to support the digital acceleration of the green transition by strengthening the data foundation for environmental data.

Privacy and Machine Learning

There is an unmet need for decentralised privacy-preserving machine learning. Cloud computing has great potential, however, there is a lack of trust in the service providers and there is a risk of data breaches. This project aims to develop AI methods and tools that enable secure and privacy-preserving use of sensitive data for machine learning. The goal is to address the lack of trust in cloud service providers and the risk of data breaches, while still enabling the use of analytical tools. 

Machine Learning Algorithms Generalisation

AI is radically changing society and the main driver behind new AI methods and systems is machine learning. Machine learning focuses on finding solutions for, or patterns in, new data by learning from relevant existing data. Thus, machine learning algorithms are often applied to large datasets and then they more or less autonomously find good solutions by finding relevant information or patterns hidden in the data.

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.

HERD: Human-AI Collaboration: Engaging and Controlling Swarms of Robots and Drones

Today, robots and drones take on an increasingly broad set of tasks. However, such robots are limited in their capacity to cooperate with one another and with humans. This project aims to address multi-robot collaboration and design and evaluate technological solutions that enable users to engage and control autonomous multi-robot systems. 

EXPLAIN-ME: Learning to Collaborate via Explainable AI in Medical Education

Together with clinicians, this project aims to develop explanatory AI that can help medical staff make qualified decisions by taking the role as a mentor who provides feedback and advice for the clinicians. It is important that the explainable AI provides good explanations that are easy to understand and utilize during the medical staff’s workflow.

Business Transformation and Organisational AI-based Decision Making

Together with industry, the project aims to develop methods and tools that enable industry to develop new efficient solutions for exploiting the huge amount of business data generated by enterprise systems, with specific focus on tools and responsible methods for the use of process insights for business intelligence and transformation. 

AI and Blockchains for Complex Business Processes

Together with industry, this project aims to develop methods and tools that enable the industry to develop new efficient solutions for exploiting the huge amount of business data generated by enterprise and blockchain systems, with specific focus on tools and responsible methods for the use of process insights for business intelligence and transformation. 

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.