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In-Situ Charging of Flying Unmanned Aerial Vehicles (UAV's) Base Stations

Project Description

Recent developments in robotics, miniaturisation, sensors and communications technology have revolutionised Unmanned Aerial Vehicles (UAVs, also known as drones). Due to their deployment flexibility, low cost and programmable control features, UAVs are being considered in a wide range of applications including cellular wireless networks. In particular, the use of flying (or cruising) aerial base stations wherein the UAVs continue to service ground nodes while in flight have been shown to maximise network performance in the presence of geospatial variation in user demand, or to improve spectral efficiency. However, the success of these applications is dependent on the UAV service duration longevity, which is dependent on the capacity of the on-board battery they are powered by, especially for small size UAVs with smaller-capacity batteries.

Traditional methods of recharging UAVs require them to move away from their serving area and land on a charging pad located at a fixed spot, which disrupts service continuity. As such, developments of methods and mechanisms for in-situ recharging of UAVs are of utmost importance. This project aims to explore techniques of far-field wireless charging and develop solutions for in-situ charging of flying UAV Base Stations.

Depending on the project scope, complex issues such as recharging of multiple UAVs in the face of different and dynamic UAV trajectories, their changing battery levels and its impact on the recharging decisions, energy harvesting efficiency and improvement of the same, etc., will be addressed. As these issues are complex and can be interdependent, ‘a priori’ decisions needed by the charging solutions may not be practical, as such Deep Learning (DL) technologies will be employed to train a Neural Network agent which will be able to make decisions in an online fashion to satisfy such recharging demands. Based on the specific requirements, MATLAB or TensorFlow will be used to develop and train the DL models.

How to Apply

Research Higher Degree candidates apply via iStart with an Expression of Interest which asks you to select your course and location, along with providing a brief description of your proposed research topic. Expression of Interest must be submitted by the prospective candidate.
Depending upon the outcome of your Expression of Interest, you may be invited to submit a full application for admission as a research higher degree candidate. The University reserves the right to refuse admission to any applicant.

Application Deadlines

Our Research Higher Degree courses don’t have any deadlines as students can commences these courses at anytime.

Funding Notes

Funding is also provided by CQUniversity to support research higher degree student project costs, and to support national and international conference presentations. This includes:

For masters by research candidates:

- up to $4,000 in Candidate Support Funds
- up to $3,000 for Candidate Travel Support

For doctoral candidates:

- up to $6,000 in Candidate Support Funds
- up to $4,500 for Conference Travel Support

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