CASIA OpenIR  > 类脑智能研究中心  > 神经计算及脑机交互
杜学丹1,2; 蔡莹皓1; 鲁涛1; 王硕1; 闫哲2
Source Publication机器人
Abstract提出了一种基于深度神经网络的机械臂最优抓取位置检测方法。相比传统手工设定的特征,基于深度神经网络的方法学习得到的特征具有较强的鲁棒性和稳定性,能够适应训练集中未曾出现的新物体。本方法首先使用基于深度学习的目标检测算法对图像中的目标物体进行检测,记录目标的类别和位置。然后根据分类检测结果,使用基于深度学习的机械臂抓取方法进行抓取位置学习。仿真实验表明所提方法能对图像中的目标物体进行较为准确的分类,在Universal Robot 5 机械臂上得到的抓取实验结果证明了所提方法的有效性。
Other AbstractA method is proposed to detect the optimal position of robotic grasping based on deep neural network. Compared with conventional manually-set features, the features learned by deep neural network methods are more robust and stabler, and can be applied to objects outside of the training set. In this method, the object detection algorithm based on deep learning is first used to detect the objects in the image with the classes and locations of the objects recorded. Then, the robotic grasping method based on deep learning is used to learn the grasping positions according to the object classification and detection results. Simulation experiments indicate that the proposed method can classify the objects in the images accurately, and the grasping experimental results on Universal Robot 5 verify the effectiveness of the proposed method.
Keyword机械臂抓取 深度学习 目标检测 分类
Indexed ByEI
Document Type期刊论文
Corresponding Author蔡莹皓
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
杜学丹,蔡莹皓,鲁涛,等. 一种基于深度学习的机械臂抓取方法[J]. 机器人,2017,39(6):820-828,837.
APA 杜学丹,蔡莹皓,鲁涛,王硕,&闫哲.(2017).一种基于深度学习的机械臂抓取方法.机器人,39(6),820-828,837.
MLA 杜学丹,et al."一种基于深度学习的机械臂抓取方法".机器人 39.6(2017):820-828,837.
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