PhD Studentship: Human-Drones Teaming for Autonomous Rescue with Fast Reinforcement Learning

Applications to be received by: 01/05/2022

Cranfield Campus based students start date: June or Sept 2022   

Fee status of eligible applicants: UK

Duration of Award if full time preferred*:  4 years

Supervisors- 1st Supervisor: Prof. Weisi Guo   2nd Supervisor: Dr. Yang Xing

Sponsored by EPSRC and Thales UK, this studentship will provide a bursary of up to £15,000 (tax free) plus fees* for four years.

This is an opportunity to study for a PhD degree sponsored by EPSRC and Thales UK in the area of machine learning for search and rescue. You will be developing human-machine teaming techniques to allow human and drones to work alongside each other to rapidly improve search and rescue in natural and manmade disaster areas. The focus of the project will be on machine learning and experimental testing.

In many emerging crisis areas, the environment has changed compared to prior knowledge. This makes autonomous tasks such as search and rescue very challenging, as it cannot exploit prior maps and contextual knowledge. Human-drone teaming (HDT) can overcome some of these challenges and requires a mutual understanding between the human ground stakeholders and drones.

It is paramount to study how autonomous drones can reason the specific situation efficiently and improve the situation awareness based on the prior knowledge from the human experts to accomplish the rescue task successfully.

Cranfield overview and Sponsor Information/Background: This is an industrial Thales UK and EPSRC sponsored award, with the view to improve human-machine teaming in future autonomous systems. Thales Group employs over 80,000 people globally and has generated 18bn in revenues.

Expected impact/results of research project: Demonstrate the integration of human knowledge with machine learning in autonomous / drone systems in a changing environment.

Unique Selling Points of project (e.g.: travel, conferences, external training opps). The project is supported by a healthy amount of opportunities to experiment, travel to conferences, and be embedded with the industrial funder.

What will the student gain from experience (transferable skills, employability): The student will gain rich experiences in machine learning, real world engineering, engaging with industry and academia.

For further information please contact: Prof. Weisi Guo

Name: Prof. Weisi Guo

Email: [email protected]

T: (0) 1234 750111