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Iterative learning of human partner's desired trajectory for proactive human-robot collaboration
Version 2 2023-06-07, 08:45
Version 1 2023-06-07, 06:57
journal contribution
posted on 2023-06-07, 08:45 authored by Jingkang Xia, Deqing Huang, Yanan LiYanan Li, Na QinA 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.
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Publication status
- Published
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- Published version
Journal
International Journal of Intelligent Robotics and ApplicationsISSN
2366-5971Publisher
SpringerExternal DOI
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4Page range
229-242Department affiliated with
- Engineering and Design Publications
Full text available
- No
Peer reviewed?
- Yes
Legacy Posted Date
2020-05-06First Open Access (FOA) Date
2020-06-18First Compliant Deposit (FCD) Date
2020-05-12Usage metrics
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