Inspecting Critical Infrastructure such as Power Masts or Railway Tracks with Drones: University of Klagenfurt project receives funding from the Christian Doppler Research Association

Through its Transfer.Science to Spin-off funding scheme, the Christian Doppler Research Association (CDG) is supporting ten research activities as they take their final steps from fundamental research towards application, with a total of EUR 6.8 million in funding. Among the funded initiatives is AIONIC, a project that aims to enable the fully autonomous inspection of critical infrastructure using drones or robotic systems. The AIONIC project team — Thomas Jantos, Martin Scheiber, and Eren Allak — is led by Professor Stephan Weiss, head of the Control of Networked Systems research group. The team is building on extensive preliminary work from prior research projects.

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Flying to victory with the best drones

Gilbert Tanner is part of the SAPIENCE team at the University of Klagenfurt, a group consisting of six young students and researchers who are working on new approaches to the use of drones in search and rescue operations. However, the student, who is pursuing a Bachelor’s degree in Robotics & Artificial Intelligence, is not only launching drones into the air, but also striving for lofty goals himself: he will soon complete his studies in the shortest possible time and will then move on to pursue a Master’s degree programme at ETH Zurich.

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Team from the University of Klagenfurt wins drone competition in Huntsville, USA

Exploring and mapping an environment, locating objects and people in need, and finally bringing them first aid kits: these are the tasks set in three competitions in the SAPIENCE project. Four research teams from four universities compete against each other in these competitions in order to learn from each other. The team led by Luca Di Pierno achieved its first victory in the competitions, which took place in Huntsville, USA.

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Robots gain new function: algorithm automatically recognises sensors and their mathematical modelling

Robots need localisation algorithms to figure out where they are. These algorithms usually work with sensor data, which can be used to calculate their position. For engineers and researchers, figuring out how a sensor is built, what format the sensor data is in, and how the sensor is calibrated on a robot can be quite a challenge. Christian Brommer and his team at the Control of Networked Systems research group at the University of Klagenfurt have developed a new method that eliminates the need for all of this: the algorithm automatically recognises the sensor model and calculates important data for localisation.

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