Intelligent Control and Motion Planning for Autonomous Robots

About the Project

These projects are open to students worldwide, but have no funding attached. Therefore, the successful applicant will be expected to fund tuition fees at the relevant level (home or international) and any applicable additional research costs. Please consider this before applying.

Autonomous robotic systems require effective control and navigation algorithms to operate safely and efficiently in dynamic and uncertain environments. Navigation involves the generation of feasible motion plans, while control governs the execution of these plans in the presence of modelling uncertainties, external disturbances, and imperfect sensing. Despite significant advances in robotic perception, limitations in control and navigation remain a primary factor restricting reliable long-term autonomy.

Classical control and navigation approaches, including model-based control, trajectory optimisation, and graph-based planning, rely on accurate system and environment models. In practice, such models are often incomplete or uncertain, leading to degraded performance when robots operate outside nominal conditions. More recently, learning-based methods have been introduced to improve adaptability, allowing robots to learn control policies or navigation behaviours from data. However, purely data-driven approaches often lack stability guarantees, generalisation capability, or robustness when deployed beyond their training conditions. Understanding how to combine model-based and learning-based methods remains an open research problem in robotics.

This PhD project will investigate AI-based control and navigation methods for autonomous robotic systems, with emphasis on algorithmic development and analysis. The research will focus on the design, evaluation, and comparison of control and navigation strategies, including learning-based control, adaptive and robust control methods, and AI-assisted motion planning. Particular attention will be given to the interaction between perception outputs and control decisions, and to how uncertainty in state estimation and environment representation affects navigation performance.

The project will employ simulation-based experimentation to evaluate control and navigation algorithms under controlled and repeatable conditions. Standard robotic simulation environments and benchmark scenarios will be used to analyse stability, robustness, convergence, and performance across a range of operating conditions. This approach enables systematic investigation of algorithmic behaviour without reliance on platform-specific hardware constraints.

Where appropriate and subject to availability, selected algorithms may be evaluated on shared robotic platforms within the Robotics Group for limited experimental validation.

The expected outcome of the research is a deeper understanding of how AI-based methods can be integrated with control and navigation frameworks to improve robustness and reliability in autonomous robotic systems. The project will contribute new insights into control–navigation integration and provide validated algorithms applicable across different robotic platforms.

Decisions will be based on academic merit. The successful applicant should have, or expect to obtain, a UK Honours Degree at 2.1 (or equivalent) in Robotics, Electrical or Electronic Engineering, Mechanical Engineering, Mechatronics, Control Engineering, or a closely related discipline.

The candidate should have a solid foundation in robotics and control systems, including topics such as dynamics, feedback control, or autonomous navigation. Experience with algorithm development for robotic systems is required, with proficiency in programming languages commonly used in robotics research (e.g. Python, MATLAB, or C++).

Familiarity with robot navigation, motion planning, or control algorithms is expected. Prior exposure to learning-based control methods, optimisation, or reinforcement learning is advantageous but not mandatory. Experience with robotic simulation environments (e.g. ROS-based simulators or equivalent) is desirable.

The project does not require prior experience with specific robotic platforms. Strong analytical skills, mathematical reasoning, and the ability to conduct independent research in robotics control and navigation are essential.

Application Procedure:

Formal applications can be completed online: https://www.abdn.ac.uk/pgap/login.php.

You should apply for PhD in Engineering to ensure your application is passed to the correct team for processing.

Please clearly note the name of the lead supervisor and project titleon the application form. If you do not include these details, it may not be considered for the studentship.

Your application must include: A personal statement, an up-to-date copy of your academic CV, and clear copies of your educational certificates and transcripts.

Please note: you do not need to provide a research proposal with this application.

Informal enquiries can be made by contacting Dr A Rohan at ali.rohan@abdn.ac.uk. If you require any additional assistance in submitting your application or have any queries about the application process, please don't hesitate to contact us at researchadmissions@abdn.ac.uk

Funding Notes

This is a self-funding project open to students worldwide. Our typical start dates for this programme are February or October.

Fees for this programme can be found here Finance and Funding | Study Here | The University of Aberdeen