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Spatial iterative learning control for robotic path learning
journal contribution
posted on 2023-06-10, 02:06 authored by Lin Yang, Yanan LiYanan Li, Deqing Huang, Jingkang Xia, Xiaodong ZhouA spatial iterative learning control (sILC) method is proposed for a robot to learn a desired path in an unknown environment. When interacting with the environment, the robot initially starts with a predefined trajectory so an interaction force is generated. By assuming that the environment is subjected to fixed spatial constraints, a learning law is proposed to update the robot's reference trajectory so that a desired interaction force is achieved. Different from existing iterative learning control methods in the literature, this method does not require repeating the interaction with the environment in time, which relaxes the assumption of the environment and thus addresses the limits of the existing methods. With the rigorous convergence analysis, simulation and experimental results in two applications of surface exploration and teaching by demonstration illustrate the significance and feasibility of the proposed method.
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Publication status
- Published
File Version
- Accepted version
Journal
IEEE Transactions on CyberneticsISSN
2168-2267Publisher
IEEEExternal DOI
Page range
1-10Department affiliated with
- Engineering and Design Publications
Notes
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2022-01-04First Open Access (FOA) Date
2022-01-04First Compliant Deposit (FCD) Date
2022-01-02Usage metrics
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