Resilient autonomous navigation and semantic mapping for agriculture robots

   Department of Aeronautical and Automotive Engineering

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  Dr Cunjia Liu, Dr M Coombes  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

This PhD project aims to develop fundamental methods and practical solutions for agriculture robots to navigate through a variety of agricultural environments, including crop fields, orchards and polytunnels, and operate safely and reliably across different growing seasons. Developing a resilient Simultaneous Localisation and Mapping (SLAM), which can deal with semi-structured and partially varying environments, would be the key to this project.

We will explore the possibility of combining the recent machine learning-based semantic segmentation techniques with the pose graph SLAM framework to achieve concurrent precise localisation and semantic mapping of the environment. This is not only required to support autonomous navigation of the robots but also plays a critical role in agriculture information gathering to support agronomy decisions.

This position is for a candidate with knowledge and interest in robotics and autonomous systems, machine learning, and sensor-fusion with an appreciation of agriculture applications. The PhD student will be based in the Autonomous Systems Laboratory at Loughborough University and will be attached to two ongoing agriculture robotic projects funded by Innovate UK.

We have advanced robotic platforms to support this project, including Boston Dynamics SPOT, Clearpath Husky and Antobot Assist robot, as well as precise sensing devices such as 3D LiDAR, stereo and thermal cameras, and RTK GPS. The PhD student will also have the chance to work with our industrial partners, ranging from robotic companies, agriculture institutes, to growers, for testing and deployment of the developed systems in real-world scenarios.

Engineering has seen 100% of its research impact rated as 'world-leading' or 'internationally excellent' (REF, 2021).


Primary supervisor: Cunjia Liu

Secondary supervisor: Matthew Coombes

How to apply

All applications should be made online and must include a research proposal. Under the programme name, select 'Aeronautical and Automotive Engineering'. Please quote the advertised reference number AACME-23-017 in your application.

To avoid delays in processing your application, please ensure that you submit the minimum supporting documents.

Apply now

Computer Science (8) Engineering (12) Mathematics (25)

Funding Notes

The studentship is for 3 years and provides a tax-free stipend of £18,662 per annum for the duration of the studentship plus university tuition fees.

Where will I study?

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