Robotics for carbon-neutral fusion-energy - Understanding, modelling, and predicting the human tele-robotics operator for intelligent shared control

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This project will investigate the use of the perception of dynamic decisions and actions of operators of telerobotic systems through body motion and physiological data analysis. The models obtained through this analysis will be used to understand and predict the operator with the aim to isolate operator failure and improve mutual trust between the operator and the tele-operation system. This may also result in the support of the operator through intelligent, shared control schemes. The major use case for this problem is the MASCOT tele-manipulator system of the UK Atomic Energy Authority (UKAEA). MASCOT is a two-armed master-slave robot established and proven through thousands of successful hours of remote handling operations in reconfiguration and repair tasks within the JET (Joint European Torus) fusion reactor. We propose a system for task prediction of the MASCOT Operators using body tracking devices. A machine learning approach for suitable decision and action modelling (e.g. using Markov models) will be deployed to classify and predict future tasks of the operator. In a second step, this project may look into the adaptation of haptic feedback. It is believed that this appropriate and task-specific feedback may promote safety and efficiency of MASCOT operators during remote handling tasks and reduce human errors by actively introducing augmented haptic effects and other actions for safety and efficiency of MASCOT. This PhD will contribute to the general understanding of human-robot interaction, introduce new results on human decision and action models in such processes and may contribute to principles of shared control and semi-autonomy.