TASE_sILC_Vision.pdf (16.04 MB)
Spatial iterative learning control with human guidance and visual detection for path learning and tracking
journal contribution
posted on 2023-06-10, 04:03 authored by Xia Jingkang, Yanan LiYanan Li, Deqing Huang, Jinlong Yang, Yueyan Xing, Lei MaA popular path learning method is to use off-line programming by demonstration (PbD) to plan a rough path, but it is subjected to uncertainties in the environment so needs to be updated during the task execution. For this purpose, a spatial iterative learning control (sILC) is developed to learn an accurate path through intuitive online correction based on human-robot interaction (HRI). To improve the efficiency and accuracy of the path learning, a visual assistance system is added to HRI, which helps the robot to find the initial path point and complement the correction of the learning error. This method mitigates the requirement on classic ILC that the time period should be consistent in the repetitive interaction task and utilizes the complementary advantages of vision and force sensing, thus addressing the limitations of the vision-based or HRI methods. The rigorous proof of learning convergence and the results of the simulation and experiments on a 7-degree-of-freedom (DoF) Sawyer robot platform illustrate the significance and advantages of the proposed method.
History
Publication status
- Published
File Version
- Accepted version
Journal
IEEE Transactions on Automation Science and EngineeringISSN
1042-296XPublisher
Institute of Electrical and Electronics EngineersExternal DOI
Page range
1-13Department affiliated with
- Engineering and Design Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2022-06-23First Open Access (FOA) Date
2022-06-23First Compliant Deposit (FCD) Date
2022-06-22Usage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC