Although Luna relies on a broad range of sensors delivering real time feedback for direct control, we are planning to enhance the capabilities and real world Application. There are two distinct ways to improve those Aspect through Machine Learning (ML), on one side we use auxiliary ML models to increase the reliability of our system as a whole through using additional sensors to improve main sensors readings. The other application covers a broader range of application through learning Agent Policies that can control luna on their own and follow given commands such as moving in a certain direction or rotating on the spot. Through the implementation of digital twin we were able to test our software stack and hardware plans before starting the costly process of creating hardware prototypes, therefore increasing the amount of time we needed to get our first working system into a prototype state.

After an eventful week at the international FRE in Slovenia, we got to attend another award ceremony with artist Anas Kahal. A the first Kunstpreis TechnologieRegion Karlsruhe, the jury awarded first place to his ingenious exhibit which features one of our self-built autonomous robots. With Cassiopeia, Kahal creates a dynamic environment in which viewers are guided through found footage of the Syrian civil war by the movements of an autonomous robot.

At this year’s Field Robot Event at the University of Maribor in Slovenia, our robot impressively outperformed 14 other competitors in the computer vision-focused Task 3. The task determined the accuracy of the competing robots‘ recognition systems on a series of images of people, deer, and other objects. We also took 2nd place in the Freestyle category and 4th place in the Smart Irrigation task. We would like to thank our sponsors who supported our participation in the competition with financial resources and hardware. For a deep dive into our methodology, we will soon publish our FRE 2023 proceedings on this blog.

Last year, we published our dataset and models for image recognition during the online Field Robot Event 2021. Our blog post ended with a call for cooperation in creating a dataset and AI models for this year’s event, which has since taken place at DLG Feldtage in June. Team Carbonite from the Überlingen Students’ Research Center (SFZ Überlingen) approached and we subsequently worked together on building a realistic image dataset. As a nod to the two teamnames, this dataset shall henceforth be known by the name “Carbonaro”.