Iterative learning of human partner's desired trajectory for proactive human-robot collaboration

Xia, Jingkang, Huang, Deqing, Li, Yanan and Qin, Na (2020) Iterative learning of human partner's desired trajectory for proactive human-robot collaboration. International Journal of Intelligent Robotics and Applications, 4. pp. 229-242. ISSN 2366-5971

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Abstract

A period-varying iterative learning control scheme is proposed for a robotic manipulator to learn a target trajectory that is planned by a human partner but unknown to the robot, which is a typical scenario in many applications. The proposed method updates the robot’s reference trajectory in an iterative manner to minimize the interaction force applied by the human. Although a repetitive human–robot collaboration task is considered, the task period is subject to uncertainty introduced by the human. To address this issue, a novel learning mechanism is proposed to achieve the control objective. Theoretical analysis is performed to prove the performance of the learning algorithm and robot controller. Selective simulations and experiments on a robotic arm are carried out to show the effectiveness of the proposed method in human–robot collaboration.

Item Type: Article
Keywords: Period-varying iterative learning control, Human-robot interaction, Trajectory learning, Interaction force tracking
Schools and Departments: School of Engineering and Informatics > Engineering and Design
SWORD Depositor: Mx Elements Account
Depositing User: Mx Elements Account
Date Deposited: 06 May 2020 07:53
Last Modified: 18 Jun 2020 14:30
URI: http://sro.sussex.ac.uk/id/eprint/91173

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