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Simulation and deep learning on point clouds for robot grasping

Zhengtuo Wang (State Key Laboratory of Fluid Power and Mechatronic Systems, College of Mechanical Engineering, Zhejiang University, Hangzhou, China and Key Laboratory of 3D Printing Process and Equipment of Zhejiang Province, College of Mechanical Engineering, Zhejiang University, Hangzhou, China)
Yuetong Xu (State Key Laboratory of Fluid Power and Mechatronic Systems, College of Mechanical Engineering, Zhejiang University, Hangzhou, China and Key Laboratory of 3D Printing Process and Equipment of Zhejiang Province, College of Mechanical Engineering, Zhejiang University, Hangzhou, China)
Guanhua Xu (State Key Laboratory of Fluid Power and Mechatronic Systems, College of Mechanical Engineering, Zhejiang University, Hangzhou, China and Key Laboratory of 3D Printing Process and Equipment of Zhejiang Province, College of Mechanical Engineering, Zhejiang University, Hangzhou, China)
Jianzhong Fu (State Key Laboratory of Fluid Power and Mechatronic Systems, College of Mechanical Engineering, Zhejiang University, Hangzhou, China and Key Laboratory of 3D Printing Process and Equipment of Zhejiang Province, College of Mechanical Engineering, Zhejiang University, Hangzhou, China)
Jiongyan Yu (State Key Laboratory of Fluid Power and Mechatronic Systems, College of Mechanical Engineering, Zhejiang University, Hangzhou, China; Key Laboratory of 3D Printing Process and Equipment of Zhejiang Province, College of Mechanical Engineering, Zhejiang University, Hangzhou, China and Suzhou Zijingang Intelligent Manufacturing Equipment Co. Ltd., Suzhou, China)
Tianyi Gu (State Key Laboratory of Fluid Power and Mechatronic Systems, College of Mechanical Engineering, Zhejiang University, Hangzhou, China)

Assembly Automation

ISSN: 0144-5154

Article publication date: 28 May 2021

Issue publication date: 27 July 2021

206

Abstract

Purpose

In this work, the authors aim to provide a set of convenient methods for generating training data, and then develop a deep learning method based on point clouds to estimate the pose of target for robot grasping.

Design/methodology/approach

This work presents a deep learning method PointSimGrasp on point clouds for robot grasping. In PointSimGrasp, a point cloud emulator is introduced to generate training data and a pose estimation algorithm, which, based on deep learning, is designed. After trained with the emulation data set, the pose estimation algorithm could estimate the pose of target.

Findings

In experiment part, an experimental platform is built, which contains a six-axis industrial robot, a binocular structured-light sensor and a base platform with adjustable inclination. A data set that contains three subsets is set up on the experimental platform. After trained with the emulation data set, the PointSimGrasp is tested on the experimental data set, and an average translation error of about 2–3 mm and an average rotation error of about 2–5 degrees are obtained.

Originality/value

The contributions are as follows: first, a deep learning method on point clouds is proposed to estimate 6D pose of target; second, a convenient training method for pose estimation algorithm is presented and a point cloud emulator is introduced to generate training data; finally, an experimental platform is built, and the PointSimGrasp is tested on the platform.

Keywords

Acknowledgements

This work is financially supported by the National Natural Science Foundation of China (Grant No. 81827804), Science Fund for Creative Research Groups of National Natural Science Foundation of China (No. 51821093), and National Natural Science Foundation of China (Grant No. 51805477). We’d also like to thank the Training Platform of Robots and Intelligent Manufacturing of Zhejiang University for providing our experimental equipment.

Citation

Wang, Z., Xu, Y., Xu, G., Fu, J., Yu, J. and Gu, T. (2021), "Simulation and deep learning on point clouds for robot grasping", Assembly Automation, Vol. 41 No. 2, pp. 237-250. https://doi.org/10.1108/AA-07-2020-0096

Publisher

:

Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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