PhD in Aerospace Sciences – Coordination and Management of Air-Launched Effects

Start date:

Future airborne air-superiority systems will not consist of a single platform, but as a network of systems controlled by a primary manned platform managing multiple lower-cost semi-autonomous platforms. These platforms could take-off with the primary platform or be deployed mid-mission if required. While for certain tasks the assets could be managed by an operator or pilot, other tasks will require significant bandwidth and some form of autonomy will be required to coordinate the objectives for each platform. This PhD project will explore machine learning methods to train an agent to manage a set of known assets which may initially be in deployed or undeployed states. The trained agent will demonstrate a level of autonomy through coordinating movement, target allocation or defensive positioning based on information provided by a central sensor suite. This will then progress to multi-agent scenarios where a single distributed agent or multiple agents will be required in the event of limited communication between platforms. This will ensure that a deployed system would operate safely whether managed centrally or independently. The project will involve the following: 

• Modelling of an airborne engagement with assets that can be spawned in as required by the operator or autonomous agent 
• Derivation of objectives and action space for the secondary platforms and generation of information from sensors and data fusion (StoneSoup for trackers, navigation, radar etc) 
• Behaviour tree development for basic coordination 
• Reinforcement learning for training of an agent to move platforms to improve sensing of specified regions 
• Addition of targets and allocation of platforms to specified targets based on available information 

The Project will be co-supervised by a relevant expert from Leonardo Electronics, with opportunities for on-site visits, industrial placements and regular contact with industry professionals. 

How to Apply:  Please refer to the following website for details on how to apply: 

http://www.gla.ac.uk/research/opportunities/howtoapplyforaresearchdegree/.