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Adaptive and iterative learning control to simultaneously control end-effector force and direction by normal vectors learning

Published online by Cambridge University Press:  14 July 2023

Liang Han Open the ORCID record for Liang Han [Opens in a new window] ,
Yu Gao ,
Yunzhi Huang ,
Wenfu Xu  and
Lei He
Liang Han
Affiliation:
School of Electrical and Automation Engineering, Hefei University of Technology, Hefei, China
Yu Gao
Affiliation:
School of Electrical and Automation Engineering, Hefei University of Technology, Hefei, China
Yunzhi Huang*
Affiliation:
School of Electrical and Automation Engineering, Hefei University of Technology, Hefei, China
Wenfu Xu
Affiliation:
State Key Laboratory of Robotics and Systems, Harbin Institute of Technology, Harbin, China School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, China
Lei He
Affiliation:
School of Electrical and Automation Engineering, Hefei University of Technology, Hefei, China
*
Corresponding author: Yunzhi Huang; Email: hqyz@hfut.edu.cn
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Abstract

It is very challenging for robots to perform grinding and polishing tasks on surfaces with unknown geometry. Most existing methods solve this problem by modeling the relationship between the force sensing information and surface normal vectors by analyzing the forces on special end tools such as spherical tools and cylindrical tools and simplified friction model. In this paper, we propose a normal vectors learning method to simultaneously control end-effector force and direction on unknown surfaces. First, the relation that mapping the force sensing information to the surface normal vectors is learned from the demonstrated data on the known plane using locally weighted regression. Next, the learned relation is used to estimate surface normal vectors on the unknown surface. To improve the force control precision on the unknown geometry surface, the adaptive force control is developed. To improve the direction control precision due to friction, the iterative learning control is developed. The proposed method is verified by comparative simulations and experiments using the Franka robot. Results show that the end-effector can be controlled perpendicular to the surface with a certain force.

Keywords

normal vectors learningadaptive force controliterative learning controlunknown surface

Information

Type
Research Article
Information
Robotica , Volume 41 , Issue 9 , September 2023 , pp. 2861 - 2881
Copyright
© The Author(s), 2023. Published by Cambridge University Press

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