A hybrid human motion prediction approach for human-robot collaboration

Li, Yanan and Yang, Chenguang (2019) A hybrid human motion prediction approach for human-robot collaboration. The 19th Annual UK Workshop on Computational Intelligence (UKCI 2019), Portsmouth, UK, 4 - 6 September, 2019. Published in: Proceedings of the 19th Annual UK Workshop on Computational Intelligence. 1043 Springer ISBN 9783030299330

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Prediction of human motion is useful for a robot to collaborate with a human partner. In this paper, we propose a hybrid approach for the robot to predict the human partner’s motion by using proprioceptive and haptic information. First, a computational model is established to describe the change of the human partner’s motion, which is fitted by using the historical human motion data. The output of this model is used as the robot’s reference position in an impedance control model. Then, this reference position is modified by minimizing the interaction force between the human and robot, which indicates the discrepancy between the predicted motion and real one. The combination of the prediction using
a computational model and modification using the haptic feedback enables the robot to actively collaborate with the human partner. Simulation results show that the proposed hybrid approach outperforms impedance control, model-based prediction only and haptic feedback only.

Item Type: Conference Proceedings
Keywords: human-robot collaboration, robot control, impedance control
Schools and Departments: School of Engineering and Informatics > Engineering and Design
Related URLs:
Depositing User: Lucy Arnold
Date Deposited: 07 Aug 2019 10:26
Last Modified: 20 Nov 2019 16:24
URI: http://sro.sussex.ac.uk/id/eprint/85338

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