Machine Learning-Augmented Distributed Sensor Fusion for Autonomous Navigation in Confined Spaces

In the UK, much of the infrastructure, including tunnels, railways, bridges, mines, and pipelines, has been in service for over a century . Time and weather have caused this infrastructure to deteriorate. Maintaining this infrastructure requires long-term planning to prevent disruptions that could impact communities and businesses. Many infrastructure inspection tasks currently rely on non-autonomous robot platforms. These robots face challenges when navigating complex environments where GNSS signals are unavailable to support localization. The inertial measurement units (IMUs) used for autonomous localization on small to medium-sized inspection robots often lack the necessary accuracy for reliable positioning in these confined spaces. Additionally, existing robot navigation methods depend on isolated sensor data which can be susceptible to inaccuracies and noise. The sensor fusion techniques currently in use also do not effectively adapt to the changing surroundings which limit their effectiveness in complex infrastructure scenarios. This research aims to address these limitations and fill the gap by developing advanced distributed state space sensor fusion models that can better represent the dynamic behaviour of inspection robots. By using machine learning techniques and enabling data sharing among robot teams, the research seeks to improve the autonomous navigation capabilities of these platforms in confined environments where GNSS is unavailable.

<p style="text-align:justify"><strong>Project Aims:</strong></p> <p style="text-align:justify">The main aim of this research is to improve the navigation capabilities of autonomous robots operating in complex confined spaces through machine learning-augmented distributed sensor fusion.  </p> <p>The objectives of the research are as follows: </p> <ul> <li>To develop state-space-based sensor fusion methods that utilize machine learning to improve the navigation abilities of autonomous robots. </li> <li> To modify the developed algorithms into distributed fusion methods that allow multiple robots to share data and collaboratively estimate their positions and surroundings in real time   to enhance coordinated navigation. </li> <li> Assess the effectiveness of the developed algorithms by using real robots navigating confined space without GNSS. The metrics focus on accuracy in navigation, response time, and energy efficiency. </li> </ul> <p><strong>Potential Techniques:</strong></p> <p>The machine learning techniques considered include Radial Basis Function Neural Networks, Recursive Sparse Gaussian Processes, and Bayesian Committee Machines. For developing distributed sensor fusion methods, Invariant Kalman Filter, Diffusion Kalman filter, Covariance Intersection and Consensus Filters will be explored to enable effective information sharing and state estimation among multiple autonomous robots.  </p> <p><strong>Potential Impact:</strong></p> <p>The proposed research on improving the navigation of autonomous robots in confined spaces addresses inspection issues related to aging infrastructure in the UK. It impacts public safety and economic efficiency. The research aligns with the UN Sustainable Development Goals, particularly Goal 9, which focuses on building resilient infrastructure. By working on this, researchers will develop advanced expertise in machine learning, robotics, and real-time data processing, paving the way to global recognition in transformative fields like multi-robot systems, autonomous fleets, and sustainable robotics for confined spaces. </p>

<p>Formal applications for research degree study should be made online through the <a href="https://www.leeds.ac.uk/research-applying/doc/applying-research-degrees">University's website</a>. Please state clearly in the Planned Course of Study section that you are applying for <em><strong>EPSRC DTP Engineering & Physical Sciences</strong></em> and in the research information section that the research degree you wish to be considered for is <em><strong>Machine Learning-Augmented Distributed Sensor Fusion for Autonomous Navigation in Confined Spaces</strong></em> as well as <a href="https://eps.leeds.ac.uk/mechanical-engineering/staff/14023/dr-nabil-shaukat">Dr Nabil Shaukat</a> as your proposed supervisor. Please state clearly in the Finance section that the funding source you are applying for is <em><strong>EPSRC Doctoral Landscape Award 2025/26: Mechanical Engineering.</strong></em></p> <p>If English is not your first language, you must provide evidence that you meet the University's minimum English language requirements (below).</p> <p><em>As an international research-intensive university, we welcome students from all walks of life and from across the world. We foster an inclusive environment where all can flourish and prosper, and we are proud of our strong commitment to student education. Across all Faculties we are dedicated to diversifying our community and we welcome the unique contributions that individuals can bring, and particularly encourage applications from, but not limited to Black, Asian, people who belong to a minority ethnic community, people who identify as LGBT+ and people with disabilities. Applicants will always be selected based on merit and ability.</em></p> <p>Applications will be considered after the closing date.  Potential applicants are strongly encouraged to contact the supervisors for an informal discussion before making a formal application.  We also advise that you apply at the earliest opportunity as the application and selection process may close early, should we receive a sufficient number of applications or that a suitable candidate is appointed.</p> <p>Please note that you must provide the following documents in support of your application by the closing date of Friday 31 January 2025:</p> <ul> <li>Full Transcripts of all degree study or if in final year of study, full transcripts to date</li> <li>Personal Statement outlining your interest in the project</li> <li>CV</li> </ul>

Applicants to research degree programmes should normally have at least a first class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline. The criteria for entry for some research degrees may be higher, for example, several faculties, also require a Masters degree. Applicants are advised to check with the relevant School prior to making an application. Applicants who are uncertain about the requirements for a particular research degree are advised to contact the School or Graduate School prior to making an application.

The minimum English language entry requirement for research postgraduate research study is an IELTS of 6.0 overall with at least 5.5 in each component (reading, writing, listening and speaking) or equivalent. The test must be dated within two years of the start date of the course in order to be valid. Some schools and faculties have a higher requirement.

<p class="MsoNoSpacing">A highly competitive EPSRC Doctoral Landscape Award providing full academic fees, together with a tax-free maintenance grant at the standard UKRI rate (£19,237 in academic session 2024/25) for 3.5 years.  Training and support will also be provided.</p> <p>This opportunity is open to all applicants.  All candidates will be placed into the EPSRC Doctoral Landscape Award Competition and selection is based on academic merit.</p> <p><strong>Important: </strong>Please note that that the award does <em><strong>not </strong></em>cover the costs associated with moving to the UK.  All such costs (<a href="https://www.leeds.ac.uk/international-visas-immigration/doc/applying-student-visa">visa, Immigration Health Surcharge</a>, flights etc) would have to be met by yourself, or you will need to find an alternative funding source. </p> <p>Please refer to the <a href="https://www.ukcisa.org.uk/">UKCISA</a> website for information regarding Fee Status for Non-UK Nationals.</p>

<p>For further information about this project, please contact Dr Nabil Shaukat by email to <a href="mailto:N.Shaukat@leeds.ac.uk">N.Shaukat@leeds.ac.uk</a></p> <p>For further information about you application, please contact PGR Admissions by email to <a href="mailto:phd@engineering.leeds.ac.uk">phd@engineering.leeds.ac.uk</a></p>