To read the full version of this content please select one of the options below:

ASPW-DRL: assembly sequence planning for workpieces via a deep reinforcement learning approach

Minghui Zhao (Institute of Robotics and Automatic Information Systems, Nankai University, Tianjin, China)
Xian Guo (Institute of Robotics and Automatic Information Systems, Nankai University, Tianjin, China)
Xuebo Zhang (Institute of Robotics and Automatic Information Systems, Nankai University, Tianjin, China)
Yongchun Fang (Institute of Robotics and Automatic Information Systems, Nankai University, Tianjin, China)
Yongsheng Ou (Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China)

Assembly Automation

ISSN: 0144-5154

Article publication date: 23 August 2019

Issue publication date: 18 February 2020

Downloads
232

Abstract

Purpose

This paper aims to automatically plan sequence for complex assembly products and improve assembly efficiency.

Design/methodology/approach

An assembly sequence planning system for workpieces (ASPW) based on deep reinforcement learning is proposed in this paper. However, there exist enormous challenges for using DRL to this problem due to the sparse reward and the lack of training environment. In this paper, a novel ASPW-DQN algorithm is proposed and a training platform is built to overcome these challenges.

Findings

The system can get a good decision-making result and a generalized model suitable for other assembly problems. The experiments conducted in Gazebo show good results and great potential of this approach.

Originality/value

The proposed ASPW-DQN unites the curriculum learning and parameter transfer, which can avoid the explosive growth of assembly relations and improve system efficiency. It is combined with realistic physics simulation engine Gazebo to provide required training environment. Additionally with the effect of deep neural networks, the result can be easily applied to other similar tasks.

Keywords

Acknowledgements

This work is supported by National Natural Science Foundation of China (Grant No. U1613210 and Grant No. 61603200).

Citation

Zhao, M., Guo, X., Zhang, X., Fang, Y. and Ou, Y. (2020), "ASPW-DRL: assembly sequence planning for workpieces via a deep reinforcement learning approach", Assembly Automation, Vol. 40 No. 1, pp. 65-75. https://doi.org/10.1108/AA-11-2018-0211

Publisher

:

Emerald Publishing Limited

Copyright © 2019, Emerald Publishing Limited