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.

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

Casalino A.;Massarenti N.;Zanchettin A. M.;Rocco P.
2020-01-01

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.
2020
2020 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)
978-1-7281-6212-6
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1167921
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