PhD Studentship: PhD in an Integrated Intelligent Safe Environment (IISE) in the Nuclear Industry

The nuclear industry is increasingly seeking to deploy robotic and autonomous systems were possible to reduce worker exposure to hazardous environments, improve productivity and enhance operational resilience. Whilst significant progress has been made in remote operations, a major barrier we currently still find is the ability for robots and operators to work collaboratively within the same safety envelope.

Current safety approaches generally require physical segregation, exclusion zones or reduced robot operating modes, with, in some cases, complete removal of personnel. This can limit the potential benefits we could realise with autonomy and prevents effective human-robot coexistence in complex nuclear operations.

The IISE project is aimed at developing technologies, methodologies and assurance frameworks to enable humans and robotic systems to operate safely, efficiently and collaboratively within a shared environment.

Vision

To create a nuclear working environment in which robotic systems and operators can safely coexist and collaborate within the same safety envelope, dynamically adapting to changing conditions whilst maintaining demonstrable safety and regulatory compliance.

Research Challenge

There are some fundamental challenges that need to be considered as part of this work:

• Human behaviour is unpredictable.
• Nuclear environments are dynamic.
• Safety regulations ensure our operators face minimal risk with predictable and repeatable operations.
• Autonomous systems need to be predictable and verifiable.
• Regulatory acceptance requires demonstrable evidence of safety performance.
• The complex design of our stores make wireless communications difficult.

Research Focus Area – Digital Twin enabled Human-Robot and Sensor Collaboration

This PhD centres around the creation and management of a detailed digital twin of an environment containing human-robot interaction.

The digital twin should be a like for like representation of the area and provide real-time insights into the condition of the area, including atmosphere and temperature readings.

The digital twin should be able to detect the position and orientation of both the robot and human within the environment, and distinguish between the two in the virtual display, it would be good to explore prediction of path mapping and analysis of common routes as well to establish traffic patterns.

The system should also be able to detect and visualise potential collision points and manage these appropriately to support operator health and safety.

The digital twin should be sophisticated enough to allow for jobs to be rehearsed prior to human entry into the environment, this should be detailed to allow for as much realism as possible.
Alongside this the digital twin should be able to display the health of the sensors located within the area, providing a real time RAG status and planned maintenance requirements to ensure that components can be replaced before failure, maintaining the reliability of the digital twin as a visual representation of the real world.

Funding notes:

This position is open to UK fee payers AND international students, and the funding will cover all tuition fees and also provide a stipend to cover living costs during the student’s studies.

The successful applicants should have a first degree classified at 1st (or equivalent) in Robotics and AI or a closely related subject. An MSc in robotics or a related area is also highly desirable.

If you are interested in this position, email Dr Steve Davis (s.davis.2@bham.ac.uk) including a CV and a brief (half page maximum) statement explaining how the position is related to your background and experience.

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