Predicting the human behaviour in human-robot co-assemblies: an approach based on suffix trees

Abstract

Prediction of the human behaviour is essential for allowing an efficient human-robot collaboration. This was confirmed recently showing how scheduling approaches can significantly increase the productivity of a robotic cell by planning the robotic actions in a way as much as possible compliant with the human predicted behaviour. This work proposes an innovative approach for human activity prediction, exploiting both a-priori information and knowledge revealed during operation. The resulting approach is proved to achieve good performance through both off-line simulated sequences and in a realistic co-assembly involving a human operator and a dual arm collaborative robot.