Thomas Vögele, DFKI, Germany
(DeeperSense Coordinator)

The main objective of DeeperSense is to significantly improve the capabilities for environment perception of service robots to improve their performance and reliability, achieve new functionality, and open up new applications for robotics. DeeperSense adopts a novel approach of using Artificial Intelligence and data-driven Machine Learning / DeepLearning 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. As one of the most challenging application areas for robot operation and environment perception, DeeperSense chooses underwater robotics as a domain to demonstrate and verify this approach. The project implements DeepLearning solutions for three use cases that were selected for their societal relevance and are driven by concrete end-user and market needs. During the project, comprehensive training data are generated. The algorithms are trained on these data and verified both in the lab and in extensive field trials. The trained algorithms are 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.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101016958.