Spatial iterative learning control with human guidance and visual detection for path learning and tracking

Jingkang, Xia, Li, Yanan, Huang, Deqing, Yang, Jinlong, Xing, Yueyan and Ma, Lei (2022) Spatial iterative learning control with human guidance and visual detection for path learning and tracking. IEEE Transactions on Automation Science and Engineering. ISSN 1042-296X (Accepted)

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Abstract

A 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.

Item Type: Article
Schools and Departments: School of Engineering and Informatics > Engineering and Design
SWORD Depositor: Mx Elements Account
Depositing User: Mx Elements Account
Date Deposited: 23 Jun 2022 17:14
Last Modified: 24 Jun 2022 07:01
URI: http://sro.sussex.ac.uk/id/eprint/106568

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