[PhD by Enterprise FSE] Developing and Commercialising a UAV Perching Technology for Extended Infrastructure Inspection

About the Project

This project aims to solve mission time limitations of unmanned aerial vehicles (UAVs). Current UAVs are limited by short mission duration and reduced ability to observe or collect data over extended periods. This challenge is particularly significant in the nuclear, security, defence, and inspection sectors, where long‑term, repeatable observations are essential. For example, the leading inspection UAV, the Flyability Elios 3, has a flight time of only 6–10 minutes, meaning the actual inspection window may be as little as 1–2 minutes once transit is included. Some sensor types—such as gas sensors used during hazardous releases—also require long data‑collection periods.

Proposed solution is to enable UAVs to land on a wide variety of surfaces, including pipes and tree branches, allowing them to remain in position and maintain observation for extended periods. This capability is achieved through the use of a mechanical device that enables landing and autonomous landing software to complete landing. This project will focus on the autonomous landing software, as the mechanical device is largely complete through previous projects.

The exact aim of the project can be summarised as: The UAV autonomously identifies and lands on a suitable surface while maintaining observation of a user‑defined object or area.

This breaks down into the following objectives:

1.  Autonomous perching on a selected surface.
2.  Autonomous recognition of a perchable surface.
3.  Autonomous decision‑making to determine which perchable surface is most suitable for landing while keeping a specific object in view.

Each of these objectives is essential for autonomous perching and offers significant opportunities for research novelty, while directly contributing to the development of the product.

Applicants are expected to hold (or about to obtain) a minimum upper second-class undergraduate honours degree (or equivalent) in Aerospace, mechanical, computer science or electronic engineering Research experience in machine learning, robotics and/or aerial vehicles is desirable.

To apply for this project please select PhD Enterpise (FSE).