Sturdy Autonomous Navigation Primarily based on Synthetic Intelligence Approaches PhD – Drones Information


This is an excellent fully funded Autonomy, Navigation, and Artificial Intelligence PhD opportunity to pave the way for more comprehensive implementation of autonomous systems like drones or self-driving cars in our everyday lives.

Although these systems have been in use for a number of years, the robustness of their autonomous operation, including the ability to safely navigate complex urban environments, is still an open challenge. This project focuses on the development of a safe AI-based navigation solution for unmanned aerial vehicles (UAV), which enables reliable operation in safety-critical missions when satellite navigation such as GPS or GNSS is not available or its quality is severely impaired.

Secure navigation is one of the key technologies for emerging applications of autonomous systems such as drones and cars in the smart city and urban aerial mobility ecosystems. In addition to the economic and societal benefits, autonomous systems in these emerging areas should be able to provide resilience in the event of disasters and pandemics, for example by enabling autonomous deliveries under conditions of viral threats (such as COVID 19) without significant risks to couriers or delivery recipients.

Current solutions do not provide the required level of accuracy and resilience when satellite navigation is a challenge, e.g. in urban canyons. There is a growing and urgent need worldwide for high-precision hybrid navigation technologies that can ensure the secure performance of unmanned vehicles in autonomous, safety-critical operations. Therefore, this project aims to develop a cost-effective, safe navigation solution for autonomous systems, which is suitable for safety-critical missions in environments in which satellite-based navigation is either impaired or denied. Cranfield is an all-post graduate technology and management university widely recognized for its outstanding transformation research that serves the needs of businesses, government and society at large.

EPSRC is offering this collaboration opportunity between Cranfield and Spirent Communications, the world’s leading provider of automated testing and assurance solutions for networking, cybersecurity and positioning, as part of its funding program. This project provides a highly accurate, robust hybrid navigation and positioning solution that uses multiple sources of location and navigation information in an efficient framework based on deep learning techniques. The project also offers extensive training at both Spirent Communications and Cranfield, covering essential skills in the areas of artificial intelligence, sensor fusion, positioning, related simulation software and hardware.

At Cranfield, you have access to the university’s core competency training programs for PhD students, while Spirent facilitates the development of industry-specific transferable skills by engaging in teamwork, preparing and attending workshops, and presenting to clients. As part of this project, you will also benefit from numerous opportunities to present your work at large international conferences and industry events. In this exciting project, you will be introduced to the latest technological developments and learn from academic and industrial experts in the field.

Support from extensive training options in technical and transferable skills will help you prepare well for your future success in industry or academia.

At a glance

  • Application deadline Dec 14, 2020
  • Award type (s) PhD
  • Start date 01 Feb 2021
  • Duration of the award 3 years
  • Eligibility for UK, EU
  • Reference numberSATM176


1. Supervisor: Dr. Ivan Petrunin

2. Supervisor: Prof. Weisi Guo

entry requirements

Applicants should have a UK degree or equivalent in the UK, or an equivalent degree in a related discipline.

This project would suit someone with:

  • A strong background in computer programming (e.g. C / C ++, Python, Rust).
  • A hands-on approach with skills in implementing control / fusion / learning techniques in robotics, unmanned or autonomous systems.
  • Demonstrable knowledge of statistical modeling and data analysis.
  • Fancy equipment and electronics.
  • Familiarize yourself with working on the R&D team of engineers.


To be eligible for this funding, applicants should not have any restrictions on how long they can stay in the UK; H.:

  • Have no visa restrictions or
  • Applicant has “resident” status and has been “resident of the UK” for three years prior to commencement of studies and has not resided in the UK wholly or primarily for the purpose of full-time education (not applicable to UK or EU citizens).

Due to funding restrictions, all EU citizens are entitled to a fee-only premium if they do not have “settled” status in the UK.

About the sponsor

Sponsored by EPSRC, Cranfield University and Spirent Communication, this scholarship offers a selected grant for candidates up to ÂŁ 20,000 tax free plus fees for three years. You will have the opportunity to attend international conferences and meet industrial workers to train, guide and experiment.

Cranfield Doctoral Network

Research students at Cranfield benefit from a dynamic, focused and professional study environment and become valued members of the Cranfield Doctoral Network. This network brings both research students and staff together and offers our researchers a platform to exchange ideas and work together in a multidisciplinary environment. The aim is to promote an effective and lively research culture based on diversity of activities and knowledge. In addition to our core development program for doctoral students (training of transferable skills), a tailor-made seminar and event program offers students a wealth of social and networking opportunities.

How do I apply?

For more information please contact:

For more information please contact:
Name: Dr. Ivan Petrunin
E-mail: [email protected]
T: (0) 1234 750111 Extension: 8262

If you are eligible to apply for this scholarship, please complete the online application form.

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