About the Project
An embedded edge-intelligence framework for real-time contact-state estimation in constrained nuclear maintenance and decommissioning tasks.
- Lead Supervisor: Dr Ziwei Wang School of Engineering, Lancaster University
- Second Supervisor: Prof James Taylor; Prof Qiang Ni (Lancaster)
- Industry Partner: TG0 Ltd (Confirmed - formal agreement in progress)
- Project Start: October 2026
- Target Background: Top-tier degree in Embedded Systems, Electrical Engineering, Applied Physics or Computer Science with a focus on Edge AI.
- Industrial Funding: Indicative SME-level support (Sought)
- Advert Close Date: ASAP
- Programme: 4 year Engineering Doctorate (EngD) with industry placement
Reading contact you cannot see.
Constrained nuclear maintenance tasks often encounter failure at the point where contact must be interpreted before it can be visually confirmed. Blind insertion, seal engagement, contact-guided retrieval and confined manipulation are typical examples, where the controller must judge whether contact is stable and whether seating is complete within restricted geometries and under limited visibility.
When this judgement is poor, minor errors can quickly lead to failed execution, repeated attempts or unnecessary force applied to sensitive assets. Localised micro-cameras or borescopes are restricted by shadows, debris and optical focal limits during the final millimetres of blind insertion or seal engagement.
This EngD develops an embedded edge-intelligence framework for real-time contact-state estimation, designed to function within hazardous nuclear environments. It is validated on a Lancaster robotic manipulation platform, with TG0's tactile sensing integrated at the end-effector and paired with embedded edge hardware for local tactile inference.
Aims and objectives
- Develop physically grounded models of tactile signal formation under environmental stressors. Establish how TG0's sensing responds to deformation, contact distribution, temperature variation and potential radiation-induced material drift.
- Design lightweight, fault-tolerant embedded inference engines. Investigate compact neural models and signal-processing pipelines with fault-detection logic to mitigate transient hardware faults including single-event upsets typical of radiation-exposed COTS embedded systems.
- Validate embedded contact-state estimation on representative constrained-space tasks. Evaluate estimation accuracy, inference latency, robustness and improvements to right-first-time execution on blind insertion, seal engagement and contact-guided manipulation.
Alignment to STAND-UP impact targets
- >50% reduction in overall build or decommissioning process time
- >40% reduction in maintenance time
- >30% reduction in person hours on builds (not applicable)
Apply for this project
Contact the lead supervisor or programme team to discuss your interest. Full application instructions are on the How to Apply page.
Funding Notes
£26,000 per year, plus a significant project, travel and training budget. Open to UK home students.