Intelligent Multimodal Sensing and Motion Intention Recognition for Adaptive Human-Robot Collaboration

About the project

About the Project Applications are invited for a self-funded, 3 year full-time or 6 year part-time PhD project. The PhD will be based in the School of Computing and will be supervised by Dr Dalin Zhou , Dr Xin Zhang , and Dr Xia Han . This project investigates intelligent sensing and motion intention recognition for adaptive human-robot collaboration. By integrating multimodal sensing technologies with AI-driven data analysis, robotic control and decision-making, the research aims to enable robots to perceive, interpret and reliably anticipate human intentions in real time, supporting natural, safe and efficient collaboration in dynamic robot-assisted environments. Project Highlights: The work on this project will include: Development of intelligent multimodal sensing systems for human motion and intention understanding. Real-time perception, interpretation and prediction of human actions in dynamic environments. Integration of robot control and decision-making with human intention modelling. Adaptive human-robot collaboration in uncertain and unstructured scenarios. Applications in collaborative manufacturing, healthcare, rehabilitation and assistive robotics. Emphasis on safety, efficiency and natural interaction in robot-assisted environments. Project description Human-robot collaboration is becoming increasingly important in industrial, healthcare and assistive environments, where robots are required to operate safely and effectively alongside humans. A key challenge is enabling robots to understand human motion and infer underlying intentions in real time to facilitate natural and adaptive interaction rather than rigid pre-programmed behaviour. This project aims to develop intelligent sensing and motion intention recognition methods for adaptive human-robot collaboration. The research will integrate multimodal sensing technologies, including wearable sensors, with AI-driven data analysis to extract meaningful representations of human motion. Deep learning techniques will be investigated to enable robust and real-time human intention inference under complex and uncertain conditions. In addition, the project will explore the integration of intention recognition with robotic control and decision-making, enabling robots to dynamically adapt their actions in response to human behaviour. This includes developing predictive models that allow robots to anticipate human intentions and coordinate actions seamlessly in shared tasks. The proposed methods will be evaluated in dynamic robot-assisted environments, such as collaborative manufacturing, rehabilitation and human-centred service robotics. The expected outcomes include novel multimodal sensing strategies, robust motion intention recognition models, and adaptive collaboration mechanisms that improve safety, efficiency and usability in human-robot applications. General admissions criteria You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a Master’s degree in Computer Science, Robotics, Artificial Intelligence, Engineering or a related area. In exceptional cases, we may consider equivalent professional experience and/or qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0. International students will require a study visa from UKVI to pursue the degree in the UK. If the research is in a sensitive or technological subject, the student may also need to secure an Academic Technology Approval Scheme (ATAS) certificate from the UK Foreign Office. How to Apply We’d encourage you to contact Dalin Zhou ( dalin.zhou@port.ac.uk ) to discuss your interest before you apply, quoting the project code. When you are ready to apply, please follow the ' Apply now ' link on the Computing PhD subject area page and select the link for the relevant intake.. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. Our ‘ How to Apply ’ page offers further guidance on the PhD application process. When applying please quote project code CMP10660529.

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