Advanced Drone Technology for Precision Operations

This research proposal is focused on the critical objective of advancing precision in the domain of drone operations. Currently, literature predominantly focuses on discrete parameters affecting precision. Basso (2019) presents a prototype UAV that achieves high precision positioning using onboard sensors and sensor fusion techniques. Stehr (2015) discusses the use of drones in precision agriculture, highlighting their ability to monitor fields more frequently and capture detailed data for crop management. Gonzalez (2018) focuses on advances in unmanned aerial systems and payload technologies for precision agriculture. Ajakwe (2022) proposes a deep learning-based approach for proactive identification and neutralization of UAVs in mission critical operations, achieving high performance metrics in terms of detection accuracy and efficiency. Overall, these works demonstrate the potential of advanced drone technology for precise operations in various fields, yet they lack a comprehensive analysis of how these factors interact and collectively impact drone performance. To address this gap, the proposed research will systematically identify and categorize the key parameters affecting drone precision, considering environmental and technical aspects. It will develop a comprehensive testing framework and methodologies for quantifying the influence of these parameters, leveraging real-world experiments and data collection. Additionally, advanced control algorithms (involving Artificial Intelligence/Machine Learning) and sensor fusion techniques will be explored to optimize precision. The expected contributions of this research include a deeper understanding of parameter interactions, actionable insights for precision enhancement across various drone applications, and the facilitation of responsible and effective drone integration into industries where precision is of utmost importance, such as agriculture, infrastructure inspection, and emergency response.

Academic qualifications

A first-class honours degree, or a distinction at master level, or equivalent achievements in Electrical/Electronic Engineering or Computer Science or Mechanical Engineering or Robotics.

English language requirement

If your first language is not English, comply with the University requirements for research degree programmes in terms of English language.

Application process

Prospective applicants are encouraged to contact the supervisor, Dr. Luigi La Spada ([email protected]) to discuss the content of the project and the fit with their qualifications and skills before preparing an application. 

The application must include: 

Research project outline of 2 pages (list of references excluded). The outline may provide details about

• Background and motivation, explaining the importance of the project, should be supported also by relevant literature. You can also discuss the applications you expect for the project results.
• Research questions or
• Methodology: types of data to be used, approach to data collection, and data analysis methods.
• List of references

The outline must be created solely by the applicant. Supervisors can only offer general discussions about the project idea without providing any additional support.

• Statement no longer than 1 page describing your motivations and fit with the project.
• Recent and complete curriculum vitae. The curriculum must include a declaration regarding the English language qualifications of the candidate.
• Supporting documents will have to be submitted by successful candidates.
• Two academic references (but if you have been out of education for more than three years, you may submit one academic and one professional reference), on the form can be downloaded here.

Applications can be submitted here.

Download a copy of the project details here.