Using AI To Optimize the Environmental Perception of Robotic Underwater Vehicles
The new DeeperSense project, an international consortium led by the German Research Center for Artificial Intelligence, is working on technologies that combine the strengths of visual and acoustic sensors with the help of artificial intelligence. The aim is to significantly improve the perception of robotic underwater vehicles.
Whether in murky waters, cramped ship tanks, or caves with little light, poor visibility conditions have a huge effect on the environmental perception of autonomous robots.
How can they still operate safely and reliably? The new DeeperSense project, an international consortium led by the German Research Center for Artificial Intelligence (DFKI), is working on technologies that combine the strengths of visual and acoustic sensors with the help of artificial intelligence. The aim is to significantly improve the perception of robotic underwater vehicles in three use cases from the maritime sector. The project is funded by the EU with around €3 million.
The possible applications of robotic systems are numerous. However, in practice, there is often a lack of functional technologies that enable autonomous robots to comprehensively perceive complex environments. Visual sensors such as cameras, which are used in robotics for tasks such as autonomous navigation, manipulation, mapping and object recognition, provide detailed information about the environment. However, their performance is highly dependent on the prevailing light and visibility conditions. In contrast, acoustic sensors for distance measurement are independent of visibility conditions. They also generate image-like data but with a significantly lower resolution than cameras. In addition, their functionality is severely limited at close range.
This is where the DeeperSense project, which started on 1 January 2021, comes into play. The overall objective of the project, which is funded under the EU's Horizon 2020 research framework program, is to significantly improve the environmental perception of field robots, especially in complex and unstructured environments such as under water. To achieve this, the project pursues an innovative approach: by using artificial intelligence (AI), in particular deep learning as a data-driven machine learning method, it aims to combine the strengths of nonvisual and visual sensors to optimize their environment perception capabilities beyond those of the individual sensors. This should not only significantly increase the performance and reliability of autonomous systems but also open up new functionalities and applications for robotics.
The DeeperSense concept will be demonstrated in three use cases from the particularly demanding underwater sector: diver monitoring in turbid waters, exploration of coral reefs, and seabed mapping. For this purpose, the project consortium brings together leading experts in the fields of maritime robotics, artificial intelligence, and underwater sensor technology—the DFKI Robotics Innovation Center, the University of Girona, the University of Haifa, and Kraken Robotik—with end users from the three different application areas—the German Federal Agency for Technical Relief, the Israel Nature and Parks Authority, and Tecnoambiente.