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Optimal Control of Autonomous Vehicles in Highly Dynamic Environments

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  • Full or part time
    Prof J Fliege
    Dr T Martinez-Sykora
  • Application Deadline
    No more applications being accepted
  • Competition Funded PhD Project (Students Worldwide)
    Competition Funded PhD Project (Students Worldwide)

About This PhD Project

Project Description

Swarms of autonomous flying vehicles have manifold applications, some of them only about to emerge: providing traffic data, environmental control (coastal areas, oil spills), surveillance and security of industrial estates, disaster management (forest fires, damage assessment after earthquakes and floods), wildlife observation, and defense. Providing such swarms with enough computational intelligence to automatically steer the independent vehicles according to the given goals is, however, a challenging task. Only recently have control algorithms emerged that work in soft-real time (i.e. minutes or seconds, as opposed to hours) and are computationally feasible for the limited resources of on-board computational units. However, the corresponding mathematical models and algorithms generally work under the assumption that the environment is generally static or displays only a limited amount of dynamism: waypoints to be visited do not change according to new information, features to be observed do not move or move only slowly, wind fields encountered are generally static and do not change suddenly, i.e. there no wind gusts etc.

This project expands on previous work in this area at the University of and will consider mathematical models and computational tools for robust control of autonomous vehicles in highly dynamic environments. In particular, the project will develop
• mathematical model formulations for corresponding autonomous vehicle routing and trajectory optimisation that take uncertainty in the environment into account (unknown waypoints and observation stations, unknown and spatial variable risks, unknown weather, etc);
• solution algorithms for vehicle routing and trajectory optimisation that take uncertainty in the model formulation into account;


DIAMOND: from Data and Intelligence via ModelliNg to Decisions

This project is part of the Southampton DIAMOND initiative of industrially funded PhD projects in Operational Research, Data Science, and mathematical modeling. This year, eight funded studentships are available within DIAMOND.

CORMSIS, the Centre for Operational Research, Management Science, and Information Systems

You will be part of the vibrant research environment of CORMSIS, the Centre for Operational Research, Management Science, and Information Systems at the University of Southampton. CORMSIS has an established breadth and depth in Operational Research unrivalled in the UK. Our research centre applies advanced mathematical and analytical modelling to help people and organisations make better decisions. CORMSIS is the largest Operational Research group in the UK, spanning Mathematical Sciences and Southampton Business School. Among the many areas of expertise, it has extensive breadth and depth of experience in mathematical modelling and optimisation, but covers the whole spectrum of current OR/MS/IS from mathematical optimisation through business analytics and simulation to qualitative research in problem structuring.. In the QS World Rankings by Subject 2019, Operational Research and Statistics at the University of Southampton are placed at 48th in the world and 7th in the UK.
(http://www.southampton.ac.uk/cormsis/)

How to apply:

Scholarships will be awarded on a competitive basis. Applicants should have or expect to obtain the equivalent of a UK first class or upper second class honours degree (and preferably a master’s degree) in mathematics, computer science, engineering or other relevant discipline. The studentship provides a maintenance grant at the Research Council UK rate and tuition fees at the UK/EU rate. Applications should include a cover letter, CV, detailed academic transcripts and the contact details for at least two academic referees.


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