Autonomous Systems and Control Research Group

Driving technological innovation and intelligent solutions that enhance safety, efficiency and sustainability
USV Cetus & Falcon Spirit
The Autonomous Systems and Control Research Group's overarching goal is to become global leaders in advancing the theory and application of autonomous systems and control, driving technological innovation and intelligent solutions that enhance safety, efficiency and sustainability, ultimately improving quality of life while addressing critical societal challenges.
Our mission is to conduct high-quality research in autonomous systems and control, developing cutting-edge solutions across these broad areas to contribute to societal wellbeing and assist industries in operating more efficiently and sustainably.
 

Areas of expertise

  • 3D modelling and point cloud processing
  • AI and computer vision for tracking purposes in navigation
  • Computer vision and feature extraction for disease classification in agritech and damage detection in offshore renewable energy (ORE) wind turbines
  • Control applications in robotics
  • Intelligent control approaches
  • Linear and nonlinear control approaches for physical systems
  • Modelling and simulation of dynamical systems
  • Operation and guidance, navigation, and control of marine autonomous systems
  • Quality of service (QoS) and quality of experience (QoE) in multimedia networks
  • Soft robotics
 

Explore our research projects

Partnering for choice and change: Education for social inclusion
Partnering for choice and change: Education for social inclusion
Controlled environments and agri-tech
Optimisation of Controlled Environment Agriculture (CEA) for Enhanced WEFE Nexus Performance
underwater ecosystem
Harnessing AI for Enhanced Underwater Biodiversity Monitoring
  • Drone-based Wind Turbine Surface Damage Detection, Application to Supergen ORE Hub ECR Research Fund: “ECRRF2024 – (Call 7), 2024–2025, £4,093.
  • Real-time Fall Prediction and Detection System for The Elderly in Care Homes Based on Quantum Computing and Artificial Vision, SECaM PhD Studentship, University Research Studentship (URS) fund General, 2024–2027, £77,902.
  • AKTP Traceability Tool for Net-Zero Cotton Supply Chains 2024–2025, £33,000
  • Embedding Systemic Inclusion for Neurodiverse and Disabled Engineering Students – how to guide, January 2025 – July 2025, £20,000

Autonomous Systems Test Laboratory

The lab is designed to test a diverse range of technologies, including robots, autonomous and teleoperated vehicles, as well as wearable devices. It provides a controlled environment for the safe operation, testing, and analysis of these systems, enabling the interaction of land, underwater, and aerial systems, and offering a broad range of opportunities for academic research and practical applications.
The facility fosters collaboration with industry partners to explore innovations in fields such as agricultural robotics, marine conservation, and digital environmental intelligence. It is also connected with other campus facilities, such as the Immersive Visualisation Suite , to facilitate the exchange of live data, enhancing the scope and impact of research and development in robotics, including emerging areas like floating offshore wind technology.
Autonomous Systems Test Lab

Our researchers

Recent publications
NovakiRibeiro, L., Borja, P., Della Santina, C., & Deutschmann, B. (2025). Singular-Perturbation Control of a Tendon-Driven Soft Robot: Theory and Experiments. IEEE Transactions on Control Systems Technology. (Early Access)
Mattioni, M., & Borja, P. (2025). Digital passivity-based control of underactuated mechanical systems. Automatica, 173, 112096
Zoghlami, F., Bazazian, D., Masala, G., Gianni, M., & Khan, A. (2024). ViGLAD: Vision Graph Neural Networks for Logical Anomaly Detection. IEEE Access
Best, O., Khan, A., Sharma, S., Collins, K., & Gianni, M. (2024). Leading Edge Erosion Classification in Offshore Wind Turbines Using Feature Extraction and Classical Machine Learning. Energies, 17(21), 5475
Tudesco, D. M., Deshpande, A., Laghari, A. A., ... & Khan, A. (2024). Utilization of Deep Learning Models for Safe Human‐Friendly Computing in Cloud, Fog, and Mobile Edge Networks. Applying artificial intelligence in cybersecurity analytics and cyber threat detection, 221-248
de Jesus, M. A., Laghari, A. A., Khan, A. A., Jumani, A. K., ... & Khan, A. (2024). Security in Blockchain‐Based Smart Cyber‐Physical Applications Relying on Wireless Sensor and Actuators Networks. Applying Artificial Intelligence in Cybersecurity Analytics and Cyber Threat Detection, 279-310
Laghari, A. A., Zhang, X., Shaikh, Z. A., Khan, A., Estrela, V. V., & Izadi, S. (2024). A review on quality of experience (QoE) in cloud computing. Journal of Reliable Intelligent Environments, 10(2), 107-121, 2024
Sorensen-Pound, A., Khan, A., & Sharma, S. (2024). Air Quality Prediction Using Edge-Gathered Traffic Data. In 2024 UKACC 14th International Conference on Control (CONTROL) (pp. 221-226). IEEE
Gonzalez-Jimenez, A., Lionetti, S., Bazazian, D., Gottfrois, P., ... & Navarini, A. (2024). Hyperbolic Metric Learning for Visual Outlier Detection. ECCV
Varga, M. N., & Bazazian, D. (2024). Interactive Heritage Site Mobile Application on Artworks. In Advances in Representation: New AI-and XR-Driven Transdisciplinarity (pp. 125-139). Cham: Springer Nature Switzerland
Jones, R., Baxter, R., Varga, M. N., Hagen, O., Aly, A., Bazazian, D., ... & Gaudl, S. (2024). Intergenerational technology codesign in deprived coastal regions
Selvaratnam, D., & Bazazian, D. (2024). Localised-NeRF: Specular Highlights and Colour Gradient Localising in NeRF. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2791-2801)
Hagen, O., Varga, M., Baxter, R., Jones, R., Aly, A., Bazazian, D., ... & Reyes, A. V. (2024). Insights from co-design of underwater telepresence and extended reality technologies with digitally excluded older adults. International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE, 298–303
Vacchini, E., Cucuzzella, M., Borja, P., & Ferrara, A. (2024). Robust Voltage Regulation for DC Microgrids via Passivity-Based Sliding Mode Control. In 2024 17th International Workshop on Variable Structure Systems (VSS) (pp. 273-278). IEEE
Winter-Glasgow, T., & Borja, P. (2024). Designing and Manufacturing Low-Cost, Tendon-Driven Soft Robots. In Annual Conference Towards Autonomous Robotic Systems (pp. 254-265). Cham: Springer Nature Switzerland
Franco, E., & Borja, P. (2024). Integral IDA-PBC of Underactuated Mechanical Systems with Actuator Dynamics and Uncertain Coupling. IFAC-PapersOnLine, 58(6), 19-24
Borja, P. (2024). Interconnection and Damping Assignment Passivity-Based Control Without Partial Differential Equations. In 2024 UKACC 14th International Conference on Control (CONTROL) (pp. 131-136). IEEE
Liu, J., Borja, P., & Della Santina, C. (2024). Physics‐informed neural networks to model and control robots: A theoretical and experimental investigation. Advanced Intelligent Systems, 6(5), 2300385.
 
 
 
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