About the Project
This cognitive robotics team applies cutting-edge Artificial Intelligence (AI) and cognitive robotics to solve critical operational and environmental challenges across modern agriculture and manufacturing. As industries shift toward sustainable, human-centric, and highly resilient practices, there is a vital need for autonomous systems capable of complex decision-making.
Applicants may choose to focus on one of three embedded projects:
- Project 1: AI-Driven Robotics for Sustainable Precision Agriculture
This project develops autonomous, vision-based robotic systems for highly targeted resource management (e.g., spot-spraying weeds). It aims to significantly reduce urban herbicide use by overcoming technical hurdles in visual complexity and high-speed precision actuation.
Supervisory Chair: Dr. Justin Quinn
- Project 2: LLM-Integrated Robotics for Industry 5.0
Focused on human-robot collaboration, this work upgrades legacy Industry 4.0 robots using Large Language Models (LLMs). By creating a novel translation framework, it allows existing robots to understand natural language instructions, bypassing the need for costly new systems.
Supervisory Chair: Dr. Nazmul Siddique
- Project 3: AI-Enabled Predictive Robotics Framework for Resilient Manufacturing
This project designs a robot-assisted predictive maintenance system. It uses sensor data to continuously forecast the Remaining Useful Life (RUL) of production assets, autonomously scheduling and deploying robots for safe, timely interventions to maximise factory uptime.
Supervisory Chair: Dr. Deepika Nikam
References
Azghadi, M. R., Olsen, A., Wood, J. C., Saleh, A., Calvert, B., Granshaw, T., Fillols, E., & Philippa, B. (2024). Precision robotic spot-spraying: Reducing herbicide use and enhancing environmental outcomes in sugarcane. Computers and Electronics in Agriculture, 235, 110365.
Wang, Jiaqi, et al. "Large language models for robotics: Opportunities, challenges, and perspectives." Journal of Automation and Intelligence, vol. 4, 2025.
Achouch, M., Dimitrova, M., Ziane, K., et al. (2022). On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges. Applied Sciences, 12(16), 8081.
