Graphene Flagship Core Project 3

This grant will cofund the establishing of a mini-CDT with 5 PhD studentships in Graphene materials at QMUL.

Drinking Water from Rain: a standalone green energy powered rainwater purification system

Hierarchical optimal energy management of electric vehicles

It has been widely recognized that vehicle electrification provides a potential way for the EU to move towards a more decarbonized transport system and sustainable circular economy. The overarching objective of this project is to develop a novel computationally efficient hierarchical adaptive optimal control framework incorporating transportation information and drivers’ habits suitable for energy management of multi-source EVs.

The National Centre for Nuclear Robotics (NCNR)

Nuclear facilities require a wide variety of robotics capabilities, engendering a variety of extreme RAI challenges. NCNR brings together a diverse consortium of experts in robotics, AI, sensors, radiation and resilient embedded systems, to address these complex problems. In high gamma environments, human entries are not possible at all. In alpha-contaminated environments, air-fed suited human entries are possible, but engender significant secondary waste (contaminated suits), and reduced worker capability. We have a duty to eliminate the need for humans to enter such hazardous environments wherever technologically possible. Hence, nuclear robots will typically be remote from human controllers, creating significant opportunities for advanced telepresence. However, limited bandwidth and situational awareness demand increased intelligence and autonomous control capabilities on the robot, especially for performing complex manipulations. Shared control, where both human and AI collaboratively control the robot, will be critical because i) safety-critical environments demand a human in the loop, however ii) complex remote actions are too difficult for a human to perform reliably and efficiently.

Predictive Modelling for Nuclear Engineering (Early Career Fellowship)

To develop numerical models that predict the coupled neutronics-thermal hydraulics physics with nuclear reactor cores and to quantify the uncertainties in these predictions.

Launch & Recovery Enhanced Sea States

The project aims to develop a novel approach to predicting a suitable time instant at which to initiate an Launch and Recovery operation, together with a confidence measure, and then to control the execution of the subsequent lift operation once initiated.

Adaptive hierarchical model predictive control of wave energy converter - Stage 3

TRANSIT

TRANSIT (Towards a Robust Airport Decision Support System for Intelligent Taxiing) is a four site project between Queen Mary University of London, the University of Sehffield, University of Stirling and Cranfield University, funded by the UK EPSRC (grant numbers EP/N029496/1, EP/N029356/1, EP/N029577/1). The lead at each site is, respectively, Dr Jun Chen, Prof Mahdi Mahfouf, Dr John Woodward and Dr Mudassir Lone, with Dr Chen as the overall project director. The project also has an extensive list of industrial partners, which currently includes Air France – KLM, BAE Systems, Manchester Airport Plc, Rolls-Royce Plc, Simio LLC and Zurich Airport. The TRANSIT project aims to develop a unified routing and scheduling system which will be more Realistic, Robust, Cost-effective and Configurable, producing better conformance of flight crew in response to 4 Dimensional Trajectories.

Electroadhesion for anchoring robots in challenging environments