A Hybrid Physical and Data-driven Approach to Motion Prediction and Control in Human-Robot Collaboration
Logos
ISBN 978-3-8325-5484-2
Standardpreis
Bibliografische Daten
Fachbuch
Buch. Softcover
2022
In englischer Sprache
Umfang: 212 S.
Format (B x L): 14,5 x 21 cm
Verlag: Logos
ISBN: 978-3-8325-5484-2
Weiterführende bibliografische Daten
Das Werk ist Teil der Reihe: Forschungsberichte aus dem Lehrstuhl für Regelungssysteme; 22
Produktbeschreibung
The first part focuses on online human motion prediction. A comprehensive study on various motion prediction techniques is presented, including their scope of application, accuracy in different time scales, and implementation complexity. Based on this study, a hybrid approach that combines physically well-understood models with data-driven learning techniques is proposed and validated through a motion data set.
The second part addresses interaction control in human-robot collaboration. An adaptive impedance control scheme with human reference estimation is presented. Reinforcement learning is used to find optimal control parameters to minimize a task-orient cost function without fully knowing the system dynamic.
The proposed framework is experimentally validated through two benchmark applications for human-robot collaboration: object handover and cooperative object handling. Results show that the robot can provide reliable online human motion prediction, react early to human motion variation, make proactive contributions to physical collaborations, and behave compliantly in response to human forces.
Autorinnen und Autoren
Produktsicherheit
Hersteller
Logos Verlag Berlin GmbH
Georg-Knorr-Str. 4, Geb. 10
12681 Berlin, DE
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