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Simulation and deep learning on point clouds for robot grasping
Robotic Intelligence and Automation ( IF 2.1 ) Pub Date : 2021-05-28 , DOI: 10.1108/aa-07-2020-0096
Zhengtuo Wang , Yuetong Xu , Guanhua Xu , Jianzhong Fu , Jiongyan Yu , Tianyi Gu

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.



中文翻译:

机器人抓取点云仿真与深度学习

目的

在这项工作中,作者旨在提供一套生成训练数据的便捷方法,然后开发一种基于点云的深度学习方法来估计机器人抓取的目标姿态。

设计/方法/方法

这项工作提出了一种用于机器人抓取的点云上的深度学习方法 PointSimGrasp。在PointSimGrasp中,引入了点云模拟器生成训练数据,并设计了基于深度学习的姿态估计算法。使用仿真数据集训练后,姿态估计算法可以估计目标的姿态。

发现

实验部分搭建了一个实验平台,包含一个六轴工业机器人、一个双目结构光传感器和一个倾斜度可调的基础平台。在实验平台上建立了一个包含三个子集的数据集。经过仿真数据集训练后,PointSimGrasp在实验数据集上进行测试,得到平均平移误差约2-3mm,平均旋转误差约2-5度。

原创性/价值

贡献如下:首先,提出了一种基于点云的深度学习方法来估计目标的6D位姿;其次,提出了一种方便的姿态估计算法训练方法,并引入点云模拟器生成训练数据;最后搭建了一个实验平台,并在该平台上对PointSimGrasp进行了测试。

更新日期:2021-07-24
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