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Multiple peg-in-hole compliant assembly based on a learning-accelerated deep deterministic policy gradient strategy

Xinwang Li (Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin, China)
Juliang Xiao (Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin, China)
Wei Zhao (Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin, China)
Haitao Liu (Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin, China)
Guodong Wang (Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin, China)

Industrial Robot

ISSN: 0143-991x

Article publication date: 27 July 2021

Issue publication date: 3 January 2022

409

Abstract

Purpose

As complex analysis of contact models is required in the traditional assembly strategy, it is still a challenge for a robot to complete the multiple peg-in-hole assembly tasks autonomously. This paper aims to enable the robot to complete the assembly tasks autonomously and more efficiently, with the strategies learned by reinforcement learning (RL), a learning-accelerated deep deterministic policy gradient (LADDPG) algorithm is proposed.

Design/methodology/approach

The multiple peg-in-hole assembly strategy is designed in two modules: an advanced planning module and a bottom control module. The advanced module is completed by the LADDPG agent, which is used to derive advanced commands based on geometric and environmental constraints, that is, the desired contact force. The bottom-level control module will drive the robot to complete the compliant assembly task through the adaptive impedance algorithm according to the command set issued by the advanced module. In addition, a set of safety assurance mechanisms is developed to safely train a collaborative robot to complete autonomous learning.

Findings

The method can complete the assembly tasks well through RL, and it can realize satisfactory compliance of the robot to the environment. Compared with the original DDPG algorithm, the average values of the instantaneous maximum contact force and contact torque during the assembly process are reduced by approximately 38% and 74%, respectively.

Practical implications

The entire algorithm can also be applied to other robots and the assembly strategy can be applied in the field of the automatic assembly.

Originality/value

A compliant assembly strategy based on the LADDPG algorithm is proposed to complete the automated multiple peg-in-hole assembly tasks.

Keywords

Acknowledgements

This work is partially supported by National Natural Science Foundation of China (Grants 91948301 and 51721003). Conflict of Interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Citation

Li, X., Xiao, J., Zhao, W., Liu, H. and Wang, G. (2022), "Multiple peg-in-hole compliant assembly based on a learning-accelerated deep deterministic policy gradient strategy", Industrial Robot, Vol. 49 No. 1, pp. 54-64. https://doi.org/10.1108/IR-01-2021-0003

Publisher

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Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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