R&D REPOSITORY

The main objective of DeeperSense is to significantly improve the environment perception capabilities of service robots and therefore improving their performance and reliability, achieving new functionality, and opening up new applications for robotics. DeeperSense adopts a novel approach of using Artificial Intelligence and data-driven Machine Learning / Deep Learning to combine the capabilities of non-visual and visual sensors with the objective to improve their joint capability of environment perception beyond the capabilities of the individual sensors. DeeperSense will focus on underwater robotics as a domain to demonstrate and verify this approach as it is considered one of the most challenging application areas for robot operation and environment perception. The project implements Deep Learning solutions for three use cases that were selected for their societal and environmental relevance and are driven by concrete end-user and market needs. During the project, comprehensive training data will be generated, where the developed algorithms will be trained upon and verified both in the lab and in extensive field trials. The trained algorithms will be optimized to run on the on-board hardware of underwater vehicles, thus enabling real-time execution in support of the autonomous robot behaviour. Both, the algorithms and the data will be made publicly available through online repositories embedded in European research infrastructures. The DeeperSense consortium consists of renowned experts in robotics and marine robotics, artificial intelligence, and underwater sensing. The research and technology partners are complemented by end-users from the three use case application areas. Among others, the dissemination strategy of DeeperSense has the objective to bridge the gap between the European robotics and AI communities and thus strengthen European science and technology.

New pathogens heighten the risk for future outbreaks. Ebola, MRSA and the Corona virus underline how highly transmissible and resistant viruses can constrain decontamination. They cause large sample sizes, are difficult to disinfect from the surroundings and easily cross borders. First responders and societies thus face new risks. In order to protect them better, the whole decontamination process is currently being studied in the French-German research project DEFERM.
As part of this project a novel qPCR-detection system is being developed. It will be automated so that it may be used to analyse multiple samples for up to 12 pathogens in parallel and within 45 minutes. The results will allow first-responders to choose their decontamination set-up accordingly. Disinfection steps target resistant and contagious pathogens and are designed to be deployed in a contactless manner. Enriched by an analysis of crisis management in France and Germany, the project is expected to strengthen international cooperation in the face of novel pandemics.