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Data-Driven Motion-Force Control Scheme for Redundant Manipulators: A Kinematic Perspective
Fan, Jialiang1,2; Jin, Long3,4; Xie, Zhengtai1; Li, Shuai1; Zheng, Yu5
2022-08-01
摘要Redundant manipulators play a critical role in industry and academia, which can be controlled from the kinematic or dynamic perspective. The motion-force control of redundant manipulators is a core problem in robot control, especially for the task requiring keeping contact with objectives, such as cutting, polishing, deburring, etc. However, when a manipulator's model structure is unknown, it is challenging to take motion-force control of redundant manipulators. This article proposes a data-driven-based motion-force control scheme, which solves the motion-force control problem from the kinematic perspective. The scheme can take effect and estimate the structure information, i.e., the model parameters involved in the forward kinematics when the structure of the manipulator is incomplete or unknown. A recurrent neural network is devised to find the solution to the scheme. Besides, the theoretical analysis is presented to prove the correctness of the scheme. Simulations and physical experiments running on seven degrees of freedom redundant manipulators illustrate the superb performance and practicability of the scheme intuitively. The key contribution of this article is that, for the first time, a motion-force control scheme aided with data-driven technology is proposed from a kinematic perspective for the redundant manipulators.
关键词Manipulators Kinematics End effectors Force Jacobian matrices Robots Force control Data driven kinematic control motion-force control recurrent neural network (RNN) redundant manipulators
DOI10.1109/TII.2021.3125449
发表期刊IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
ISSN1551-3203
卷号18期号:8页码:5338-5347
通讯作者Jin, Long(jinlongsysu@foxmail.com)
收录类别SCI
WOS记录号WOS:000793847600034
语种英语