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Collaborative mapping of large scale outdoor environments

   Department of Computer Science

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  Dr Myra Wilson, Dr F Labrosse  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

The UKRI CDT in Artificial Intelligence, Machine Learning and Advanced Computing (AIMLAC) aims at forming the next generation of AI innovators across a broad range of STEMM disciplines. The CDT provides advanced multi-disciplinary training in an inclusive, caring and open environment that nurture each individual student to achieve their full potential. Applications are encouraged from candidates from a diverse background that can positively contribute to the future of our society. 

The UK Research and Innovation (UKRI) fully-funded scholarships cover the full cost of tuition fees, a UKRI standard stipend of £15,921 per annum and additional funding for training, research and conference expenses. The scholarships are open to UK and international candidates.

Closing date for applications is 12 February 2022. For further information on how to apply please click here and select the "UKRI CDT Scholarship in AIMLAC" tab.

Project Overview

Large outdoors environments often present variable types of land and terrain, both natural and artificial.

For many applications (e.g. search and rescue, military, and farming), area mapping is essential but difficult to achieve using solely ground-based vehicles due to the variability of the terrain.

This project proposes the use of a swarm of Unmanned Aerial Vehicles (UAV's) (fixed wing or multi-copters) to provide a comprehensive coverage to precisely and accurately map the area in combination with ground based vehicles where appropriate. Previous work has investigated the use of Evolutionary Algorithms (EAs) to provide reliable communication between UAV's and ground-based vehicles in simulated environments [1]. This project would build on this work in a novel direction on real hardware.

Each UAV will be equipped with a camera and a small computer (eg Raspberry Pi) as well as data communication hardware. The UAV will perform real-time image analysis to decide where to fly in relation to the ground to accurately capture its features. For example, the UAV would fly at a lower altitude above highly textured areas, or to fly along tracks [2]. The data will be relayed to a base station using radio communication, possibly using other UAV's as relays to the base station. An EA will be used to position the drones to maximise the radio coverage of the swarm. The data collected can be used to map the area, and the UAV's and ground-based vehicles can be directed towards areas of interest.


[1] “UAV Flight Coordination for Communication Networks: Genetic Algorithms versus Game Theory”, 2021, Giagkos, A., Tucci, E., Wilson,M.S., and Charlesworth,P. In: Soft Computing, p. 1-21,
[2] "Automatic driving on ill-defined roads: an adaptive,
shape-constrained, color-based method" Ososinski, M. & Labrosse, F.,
2015, In: Journal of Field Robotics. 32, 4, p. 504-533 30 p.

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