学术文献库

Collaborative robotic systems will be a key enabling technology for current and future industrial applications. The main aspect of such applications is to guarantee safety for humans. To detect hazardous situations, current commercially available robotic systems rely on direct physical contact to the co-working person. To further advance this technology, there are multiple efforts to develop predictive capabilities for such systems. Using motion tracking sensors and pose estimation systems combined with adequate predictive models, potential episodes of hazardous collisions between humans and robots can be predicted. Based on the provided predictive information, the robotic system can avoid physical contact by adjusting speed or position. A potential approach for such systems is to perform human motion prediction with machine learning methods like Artificial Neural Networks. In our approach, the motion patterns of past seconds are used to predict future ones by applying a linear Tensor-on-Tensor regression model, selected according to a similarity measure between motion sequences obtained by Dynamic TimeWarping. For test and validation of our proposed approach, industrial pseudo assembly tasks were recorded with a motion capture system, providing unique traceable Cartesian coordinates $(x, y, z)$ for each human joint. The prediction of repetitive human motions associated with assembly tasks, whose data vary significantly in length and have highly correlated variables, has been achieved in real time.