Research Studentship in Control Engineering
3.5-year D.Phil. studentship
Project: Distributed control of autonomous aerial vehicles
Supervisors: Prof Kostas Margellos and Prof Antonis Papachristodoulou
The objective of the project is to develop real-time algorithms for learning and distributed control of autonomous aerial vehicles connected over a network, monitoring the behaviour and tasks of ground vehicles. Control of such systems involves, among others, synchronization, formation tasks for surveillance, and collision avoidance, and as such constitutes a challenging problem for control engineering and robotics with numerous applications. This project’s focus will be on integrating and validating distributed techniques on a platform that involves unmanned ground vehicles (Husarion ROSbots) and aerial ones (capitalizing on an existing CrazyFlies quadcopter arena).
The project will focus on the development and coding of cutting-edge algorithms based on distributed and robust optimization over time-varying networks; emphasis will be given on real-time implementation and on extending their scope to deal with dynamic changing environments that capture the features of realistic urban spaces. The project will involve an interplay of theoretical developments and practical implementation on the UAV-UGV smart city platform.
Objectives
The following main objectives are envisioned:
- Development of a “smart city” platform integrating autonomous aerial vehicles to monitor and regulate ground vehicle fleets;
- Development of real-time, robust and distributed coordination algorithms;
- Investigate reinforcement learning techniques for multi-agent control and optimization;
- Development of a MATLAB based tool for real-time distributed control of autonomous aerial vehicle fleets;
- Integration of the developed tools in a “smart city” platform;
- Experimental validation of their performance, via extensive experiments involving formation, surveillance and collision avoidance.
The project is funded by MathWorks; regular interaction in terms of meetings/reports and additional supervision by MathWorks should be anticipated.
Candidate Requirements
Prospective candidates will be judged according to how well they meet the following criteria:
· A first class honours degree in Engineering, Mathematics or Computer Science;
· Experience in control theory and optimization;
· Mathematical maturity with emphasis on optimization theory;
· Excellent software skills; ability to program in MATLAB;
· Excellent English written and spoken communication skills.
The following skills are desirable but not essential:
· Software engineering background
· Prior experience with robotic systems
Application Procedure
Informal enquiries are encouraged and should be addressed to Prof Kostas Margellos ([Email Address Removed]).
Candidates must submit a graduate application form and are expected to meet the graduate admissions criteria. Details are available on the course page of the University website.
Please quote 22ENGCO_KM in all correspondence and in your graduate application.
Application deadline: noon on 27 May 2022
Start date: October 2022
Continue with Facebook
