Project Title:Learning assisted control for haptic systems over 6G networks
Description
“Immersive experience” and “Robots and autonomous systems” are pillars for the future 6G networks, and involve the transmission of high-quality video, and the combination of actual and virtual elements for the collaboration between robots and humans. Haptic controllers are essential in this context, and the 6G network should enable the transmission of the corresponding data, while the controlled system should be able to readapt to the network capabilities. Hence the motivation behind this PhD topic, on the interface between telecommunications and control domains.
The PhD will start by studying the impact of the network imperfections on the performance and optimizing the 6G network for haptic tasks. There are in general 3 data streams in haptic use cases: a) a video flow, carried in 5G/6G by the eMBB service, b) haptic data from sensors, and c) the control input, both carried by the URLLC service. Quantifying the performance of the system and optimizing the radio resource allocation among the data flows will be the first direction of this Ph.D.
On the controller side, the task itself may have to be dynamically readapted to consider the imperfections on the wireless channel. An Artificial Intelligence will be developed, where the control actions are learned from data while being fed by mathematical models to ensure their convergence and effectiveness.
