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The learning-based optimization algorithm for robotic dual peg-in-hole assembly

Zhimin Hou (State Key Laboratory of Tribology, Department of Mechanical Engineering, Tsinghua University, Beijing, China and Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing, China)
Markus Philipp (State Key Laboratory of Tribology, Department of Mechanical Engineering, Tsinghua University, Beijing, China)
Kuangen Zhang (State Key Laboratory of Tribology, Department of Mechanical Engineering, Tsinghua University, Beijing, China)
Yong Guan (Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing, China)
Ken Chen (State Key Laboratory of Tribology, Department of Mechanical Engineering, Tsinghua University, Beijing, China)
Jing Xu (State Key Laboratory of Tribology, Department of Mechanical Engineering, Tsinghua University, Beijing, China)

Assembly Automation

ISSN: 0144-5154

Article publication date: 5 October 2018

Issue publication date: 26 October 2018

Abstract

Purpose

This paper aims to present an optimization algorithm combined with the impedance control strategy to optimize the robotic dual peg-in-hole assembly task, and to reduce the assembly time and smooth the contact forces during assembly process with a small number of experiments.

Design/methodology/approach

Support vector regression is used to predict the fitness of genes in evolutionary algorithm, which can reduce the number of real-world experiments. The control parameters of the impedance control strategy are defined as genes, and the assembly time is defined as the fitness of genes to evaluate the performance of the selected parameters.

Findings

The learning-based evolutionary algorithm is proposed to optimize the dual peg-in-hole assembly process only requiring little prior knowledge instead of modeling for the complex contact states. A virtual simulation and real-world experiments are implemented to demonstrate the effectiveness of the proposed algorithm.

Practical implications

The proposed algorithm is quite useful for the real-world industrial applications, especially the scenarios only allowing a small number of trials.

Originality/value

The paper provides a new solution for applying optimization techniques in real-world tasks. The learning component can solve the data efficiency of the model-free optimization algorithms.

Keywords

Acknowledgements

The work is partially supported by National Science Foundation of China (No. 51675291, No. U1613205) and the Fund of State Key Laboratory of Tribology of China (SKLT2018C04) and Basic Research Program of Shenzhen (JCYJ20160229123030978, JCYJ20160429161539298).

Citation

Hou, Z., Philipp, M., Zhang, K., Guan, Y., Chen, K. and Xu, J. (2018), "The learning-based optimization algorithm for robotic dual peg-in-hole assembly", Assembly Automation, Vol. 38 No. 4, pp. 369-375. https://doi.org/10.1108/AA-03-2018-039

Publisher

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Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited