Cognitive Human–Robot Collaboration in Industry 5.0 Using Spatio-Temporal Knowledge and Reasoning

The vision of Industry 5.0 places humans back at the centre of manufacturing, supported by intelligent robots that work with people rather than simply replacing them. For robots to collaborate effectively with humans in dynamic industrial environments, they need the ability to understand and reason about events in space and time, ensuring safety, adaptability, and trust in collaborative tasks.

This PhD project focuses on developing methods for spatio-temporal knowledge representation and reasoning that allow mobile robots to function as cognitive collaborators in industrial environments. The research will investigate how robots can transform raw sensor data into meaningful, structured knowledge and utilise this knowledge to plan and execute tasks while interacting safely and effectively with human collaborators.

This project aims to build a spatio-temporal knowledge base that represents humans, objects, and events in industrial environments, capturing not only their locations but also how they change over time. Additionally, it will develop reasoning algorithms that combine symbolic logic with machine learning–based perception, enabling robots to make context-aware decisions and adapt dynamically to their environment. The framework will be validated by demonstrating collaborative behaviours on advanced mobile robots in both simulated environments and real-world industrial scenarios.

The successful candidate will gain hands-on experience in robotics frameworks, machine learning for perception, and AI reasoning tools. They will also learn to design and evaluate human–robot interaction, contributing to both the theoretical and practical aspects of collaborative robotics.

This project offers a unique opportunity to explore the cutting edge of human–robot collaboration, developing intelligent systems that can work alongside people in complex, dynamic industrial environments, and shaping the future of Industry 5.0.