Object Positioning by Visual Servoing Based on Deep Learning
Object Positioning by Visual Servoing Based on Deep Learning
复制标题
基于深度学习的视觉伺服物体定位
DOI：
10.9746/sicetr.55.717
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
K. Kosuge
中科院分区：
文献类型：
--
作者：
Fuyuki Tokuda;S. Arai;K. Kosuge
视觉伺服能够根据摄像机捕获的图像定位机器人。为了计算机器人的命令值，需要手工设计图像特征并提取图像特征。定位准确性在很大程度上取决于图像特征的选择。在这项研究中，我们专注于卷积神经网络（CNN）从图像中提取特征并输出角速度以控制机械手的能力。我们提出了一种基于CNN的视觉伺服技术，能够精确定位由平行夹持器抓取的无纹理物体。即使抓取位置与捕获目标图像时的位置不同，也可以实现定位。所提出的方法的定位精度进行了验证艾德的基础上的定位的对象到托盘使用六自由度机械手。我们证实，所提出的视觉伺服技术可以精确地定位物体。
Visual servoing is capable of positioning robots based on images captured by cameras. To calculate the command value for robots, hand-designed image features and extraction of image features are required. Positioning accuracy is significantly influenced by the selection of the image features. In this study, we focus on the ability of convolutional neural networks (CNN) to extract features from images and output the angular velocity to control a manipulator. We propose a visual servoing technique based on CNN enabling the precise positioning of a texture less object grasped by a parallel gripper. The positioning can be achieved even the grasping position is different from the position when the target image was captured. The positioning accuracy of the proposed method is verified based on the positioning of an object into an alignment tray using a six-DOF manipulator. We confirmed that the proposed visual servoing technique can position an object precisely.
影响因子：
9.2
作者：
Levine, Sergey;Pastor, Peter;Quillen, Deirdre
通讯作者：
Quillen, Deirdre