PhD Studentship: A Novel Control Framework for Embodied Decision-Making in Multi-Robot Coordination and Task Execution

This 3.5 year PhD project is fully funded and home students, and EU students with settled status, are eligible to apply. The successful candidate will receive an annual tax-free stipend set at the UKRI rate (£20,780 for 2025/26) and tuition fees will be paid. We expect the stipend to increase each year.

We recommend that you apply early as the advert may be removed before the deadline.

As autonomous unmanned systems see growing deployment in real-world settings, swarms of these systems are becoming essential for accomplishing tasks that cannot be handled by a single robot. In challenging missions such as search-and-rescue or large-scale surveillance—where environments are unknown, dynamic, and unstructured—robotic swarms must adapt, select appropriate actions, and respond to changing conditions.

This project aims to develop a new control framework that enables embodied decision-making in autonomous swarms, allowing them to operate with resilience, reliability, and adaptability. The work will investigate how individual and collective decision processes emerge from physical systems’ dynamical sensorimotor loops, with a focus on the tight coupling between body dynamics, sensing, and interactions among neighbouring agents and their environments. The research will exploit an interdisciplinary approach that combines control theory, nonlinear dynamical systems, robotics, and formal methods to develop principled models and algorithms for distributed decision-making in complex and uncertain environments.

Your research

The candidate will develop a novel hierarchical control framework that integrates cognitive decision-making with physical systems control, enabling safe, resilient, and scalable operation in unknown and dynamic environments. The framework will focus on two key components:
(i) low-level safe coordination control, and
(ii) high-level decision-making and task coordination.

The low-level safety layer will ensure computationally efficient and safe navigation of robot swarms under complex dynamics. In parallel, collective decision-making mechanisms (e.g., opinion dynamics) will be leveraged at the high level to coordinate the desired behaviours of multi-agent systems in response to changing internal states and external environmental conditions. Both traditional model-based approaches and modern learning-based control techniques will be employed to achieve an appropriate trade-off between reliability, performance, computational efficiency, and adaptability under uncertainty.

The candidate will be affiliated with CRADLE (Center for Robotic Autonomy in Demanding and Long-Lasting Environments - https://cradlerobotics.co.uk/) and will work closely with Work Package 2 (Architectures) and Work Package 5 (Demonstrators). With support from the CRADLE team, the project will further refine and verify the proposed control framework through formal verification techniques.

Ideal candidate

We are seeking highly motivated candidates with a strong background in control theory and practical experience in robotics. Applicants should demonstrate enthusiasm for both theoretical development and hands-on implementation. This position offers the opportunity to contribute to an emerging research area and acquire a skill set highly valued in both academia and industry.

Applicants should hold, or expect to obtain, at least an Upper Second-Class Honours degree (2:1) or a Master’s degree (or international equivalent) in Control Engineering, Robotics, Applied Mathematics, or a related quantitative discipline. Research experience in control theory or robotics is highly desirable. Prior exposure to robotic platforms, including hands-on experience with ROS, will be considered an advantage.

To apply, please contact the main supervisor, Dr Zhiqi Tang - zhiqi.tang@manchester.ac.uk. Please include details of your current level of study, academic background and any relevant experience and include a paragraph about your motivation to study this PhD project.

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