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Drone Autonomy through Probabilistic Risk Estimation

  • Full or part time
  • Application Deadline
    Monday, April 01, 2019
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Since 2017 the University of Bath has been developing an approach to drone autonomy based on probabilistic estimation of both ground and air risk, and includes the probabilistic prediction of aircraft trajectories. Once coupled with flight planning algorithms this enables autonomous flight and proportionate collision avoidance. The University has formed a spinout company, 3UG Autonomous Systems Ltd, to commercially exploit the approach. This project will investigate some of the more fundamental aspects of the approach, in particular: ethics, path planning and machine learning. The student will consider some, but not all, of these aspects based on their interests and viability at the time.

In terms of ethics, there are obvious ethical implications to autonomous operation based on probabilistic methods. In particular, in this approach, the unifying variable is liability and the path planning algorithms aim to minimise liability exposure which equates to safe operations. However this raises clear ethical questions that need to be addressed. The student will therefore build on existing ethical literature to investigate the ethical case for the approach. This will include assessment of the robustness of the approach to uncertainty in the input data and aircraft trajectory prediction through simulations with artificially altered inputs. Investigating the ethical case and quantifying the sensitivity to data noise is vital for demonstrating the robustness of autonomous operation.

In terms of path planning, the current algorithms are simplistic gradient based approaches which are effective but not necessarily optimal. The student will investigate more advanced alternatives like Rapidly-exploring Random Trees (RRT), ant-colony optimisation and A* with a potential division between a high-level (low-frequency) path planner and a low-level (high-frequency) collision avoidance algorithm. These will be investigated through simulations and ultimately flight trials where multiple aircraft will operate in near-collision or collision scenarios.

In terms of machine learning, a vital element of the autonomy is the ability to forecast aircraft trajectories including uncertainty. This currently uses simple, but robust, statistical methods. There is scope to improve on this approach through machine learning. In particular there are two aspects: (i) machine learning through direct training on the vast data set of aircraft tracking data, (ii) density informed machine learning – most aircraft are likely to follow patterns, these patterns can be identified through 3D maps of air traffic density which can inform trajectory prediction algorithms. These will be investigated through simulations using idealised and real aircraft trajectories. The optimal solution will be demonstrated in flight trials where multiple aircraft will operate in near-collision or collision scenarios.

The novelty of this work includes the basis for the approach, i.e., data-driven probabilistic methods for drone autonomy, the use of path planning algorithms on 3D density maps and 4D probability density functions, and the application of machine learning to aircraft trajectory prediction. The applications of this approach include autonomous drone operations which could potentially enable the safe integration of drones into manned airspace. This has potentially very significant implications for the regulator, in the form of a potentially safe and ethical solution to drone autonomy, society, through the services it could enable, and the economy, through the companies it could enable.

This project is associated with the UKRI CDT in Accountable, Responsible and Transparent AI (ART-AI), which is looking for its first cohort of at least 10 students to start in September 2019. Students will be fully funded for 4 years (stipend, UK/EU tuition fees and research support budget). Further details can be found at: http://www.bath.ac.uk/research-centres/ukri-centre-for-doctoral-training-in-accountable-responsible-and-transparent-ai/.

Desirable qualities in candidates include intellectual curiosity, a strong background in maths and programming experience.

Applicants should hold, or expect to receive, a First Class or good Upper Second Class Honours degree. A master’s level qualification would also be advantageous.

Informal enquiries about the project should be directed to Dr David Cleaver on email address .

Enquiries about the application process should be sent to .

Formal applications should be made via the University of Bath’s online application form for a PhD in Computer Science: https://samis.bath.ac.uk/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=RDUCM-FP01&code2=0013

Start date: 23 September 2019.

Funding Notes

ART-AI CDT studentships are available on a competition basis for UK and EU students for up to 4 years. Funding will cover UK/EU tuition fees as well as providing maintenance at the UKRI doctoral stipend rate (£15,009 per annum for 2019/20) and a training support fee of £1,000 per annum.

We also welcome all-year-round applications from self-funded candidates and candidates who can source their own funding.

How good is research at University of Bath in Aeronautical, Mechanical, Chemical and Manufacturing Engineering?

FTE Category A staff submitted: 61.00

Research output data provided by the Research Excellence Framework (REF)

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