Projects

The project aims at elaborating novel methodologies for the design of security and resilience-aware ICT infrastructures for Networked Critical Infrastructures, e.g., water plants, oil and gas pipelines, power grid, and the emerging Smart Grid.

This project aims to develop Machine Learning methods that can be applied for brain tumor segmenting on MR images.

Development and testing such control algorithms and real-time communication protocols that can be applied to network controlled systems.

Here we used two Universal robots (UR3 and UR3e) to perform an assembly during a pick and place movement. To simulate factory conditions, we used a conveyor which feeds different Arucco encoded pieces to the robotic setup. If the desired piece is detected, we estimate an ideal pickup position from which it can be lifted from the conveyor. When both arms have a correct piece, an assembly is performed. This motion requires a synchronized set of movements from the robotic arms.

The main goal of the project is the development of such control algorithms, trajectory generation algorithms and real-time communication protocols that can be applied for robotic systems that work in hazardous environment and are supervised through video-information by human operator.