Human-machine collaboration and motion planning in smart factory spaces.
The aim of this project is to reduce uncertainty and thereby increasing efficiency of human robot teams in shared spaces. This will be accomplished through technology such as modelling and predicting typical human behaviour through stated of the art machine learning approaches (e.g. deep learning), wearable sensors (IoT) and smart planning strategies (e.g. via a digital twin of the real-world system). Through using wearable this project believes to develop low cost alternatives to high precision human tracking with expensive sensors in shared spaces. To further increase the safety of human robot/machine collaborations the project could investigate the detection of anomalies of human behaviour and failure in robotic systems using the developed models. Therefore, the project will develop strategies to get robots out of their “cage" and into a save collaboration mode, reducing cost and increasing efficiency of human-machine collaboration.