PhD Studentship: Autonomous Unmanned Aerial Vehicle data analysis: is it the key to the many:1 ratio, or are we missing a step?

Supervisory Team: Dr Katie Plant, UoS TBC, Dr Mark Chattington (Thales), Dr Vicky Banks (Thales)

Project description

Within the context of ground military operations, Unmanned Aerial Vehicles (UAVs) are commonly deployed for Intelligence, Surveillance, and Reconnaissance (ISR) missions. The decision-making that ensues based on UAV data can range from collecting further intelligence, to more extreme courses of action such as air strikes and weapon deployment. Therefore, the information gathered by UAV(s) has the potential to dynamically inform the development the goals and tasks of the military command team. There is a continuous drive within the military to reduce the human-UAV team to one human operator role, whilst increasing the number of UAVs deployed within the human-robot team. Murphy and Burke (2016) referred to this goal as the many:1 ratio. Here, the continuous developments of automated capabilities - such as automatic object detection and image classification - are thought to provide the functionality that will pave the way to this many:1 ratio.

Based on the identified gaps in the literature, and imminent technological advancements, the proposed research will investigate:

• The short- and long- term capabilities of automation and implications on the role of the payload operator
• The impact of different levels of automation on the payload operator’s performance, and whether automation errors can be identified and recovered from
• The efficiency of implementing a user-centred payload operator interface on individual and team performance

The proposed research will study the impact of automated capabilities on the role of the payload operator, and identify support mechanisms that keep the operator in the loop through the medium of a role-specific user interface. The PhD will employ a mixed-methods approach, utilising relevant human factors methods depending on phase of the project, including but not limited to; Hierarchical Task Analysis, Operator Event Sequence Diagrams, interviews and observational data collection, user trials, and system-based methods. The candidate will be expected to spend time at the University of Southampton and Thales sites in Reading and others depending on data collection opportunities.

If you wish to discuss any details of the project informally, please contact Dr Katie Plant, Transportation Research Group, k.plant@soton.ac.uk

Entry Requirements

A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent).

Closing date:

Applications should be received no later than 31 August 2024 for standard admissions, but later applications may be considered depending on the funds remaining in place.

How To Apply

Apply online: Search for a Postgraduate Programme of Study (soton.ac.uk). Select programme type (Research), 2024/25, Faculty of Physical Sciences and Engineering, next page select “PhD Engineering & Environment (Full time)”. In Section 2 of the application form you should insert the name of the supervisor Katie Plant

Applications should include:

Research Proposal

Curriculum Vitae

Two reference letters

Degree Transcripts/Certificates to date

For further information please contact: feps-pgr-apply@soton.ac.uk

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