The Robotics for Extreme Environments Group invites applications for a PhD studentship to develop (semi-) autonomous control of a robotic manipulator in a challenging environment. The successful candidate will work at the RAICo1 facility in Cumbria, where they will have the opportunity to work in close partnership with robotics and vision experts from the University of Manchester, as well as industrial experts from Sellafield Ltd. and the UK Atomic Energy Authority.
The project will build on work that the University of Manchester has been involved in, related to the development and use of robotic systems in challenging environments, and in particular the characterisation, monitoring, and decommissioning of nuclear facilities. The research aim is to develop safe, assured technologiesthat utilise model-based control, combined with additional sensorial information that is processed through a machine learning element. This project will cover the following research topics:
• Development of machine-learning-based task understanding/recognition modules using proprioceptive (e.g. encoders) and exteroceptive sensors (e.g. cameras) to autonomously describe, understand, and interact with the environment.
• Combine the understanding of the environment with model-based strategies to develop partial/full automation that includes robust safety elements.
• Deploy the developed robotic manipulator system onto an existing remote glovebox system, with the manipulator being used to replace a human hand in this environment.
This project will be undertaken in the RAICo1 facility (https://hotrobotics.co.uk/facilities/university-of-manchester-2/), which is located in Whitehaven, Cumbria. The facility is a collaboration between The University of Manchester, the United Kingdom Atomic Energy Authority, the Nuclear Decommissioning Authority, AWE Nuclear Security Technologies, and Sellafield Ltd. The facility is well equipped with a range of robotic systems, including Unitree B1s and H1s, several Boston Dynamics Spot robots, and various AgileX, Clearpath, Kuka, UR, and Kinova robots. RAICo1 also has dedicated robotic test areas that include unique demonstration areas for aerial, ground, and aquatic robots. RAICo1 focuses on developing robots for nuclear decommissioning that can operate in areas inaccessible to humans. The facility is in Cumbria, next to the Lake District, which is one of the most beautiful regions of the UK.
Before you apply: We strongly recommend that you contact the supervisors for this project before you apply.
How to apply: To be considered for this project you’ll need complete a formal application through our online application portal. This link should directly open an application for FSE Bicentenary PhD.
When applying, you’ll need to specify the full name of this project, the name of your proposed supervisor/s, details of your previous study, and names and contact details of two referees. You also need to provide a Personal Statement describing the motivation to apply to the project and your CV. Your application cannot be processed without all of the required documents, and we cannot accept responsibility for late or missed deadlines where applications are incomplete.
Equality, diversity and inclusion are fundamental to the success of The University of Manchester, and are at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact. We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.
We also support applications from those returning from a career break or other roles. We consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder).
Eligibility: Applicants are expected to hold (or be about to obtain) a minimum upper second-class undergraduate honours degree (or equivalent) in Robotics, AI, or a closely related subject. Research experience in optimisation-based robot control and image-based detection/tracking is desirable.
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