CASIA OpenIR  > 毕业生  > 硕士学位论文
Thesis Advisor王硕 ; 闫哲
Degree Grantor哈尔滨理工大学
Place of Conferral哈尔滨
Keyword深度学习 智能抓取 目标检测 抓取位置检测
      1. 在抓取目标的分类与定位方面,采用基于深度学习的目标检测方法。通过建立有关抓取目标的目标检测数据集,训练用于目标检测的神经网络,并对训练所得网络模型的性能进行分析。
      2. 在目标物体上的抓取位置方面,提出基于深度学习的抓取位置检测方法。同样地,通过建立有关抓取目标的抓取位置检测数据集,训练用于抓取位置检测的神经网络,将所提方法与其他方法作对比,分析所提方法的有效性。
      3. 在抓取位置的位姿估计方面,采用坐标转换的方法,将抓取位置在图像上的二维坐标转换到在机器人下的三维坐标,并对抓取位置的姿态进行估计。
Other Abstract
      For robots, grasping objects is an important ability. After several decades, robotic grasping has made great achievements and been widely used in industry, exploration, service, military and other fields. However, because of limitations, robots are not as flexible as humans to grasp objects. Affected by various factors of the objects, such as forms, materials and weights, and the complicated and changeable environment, the robotic grasping tasks still faces severe challenges.
      In this paper, the man-machine collaborative robots are used as the targets on intelligent grasping studies and controlled to grasp classified objects in the specific working environment. The visual information is regarded as a guidance for the robots. And through the deep learning based studies of the image feature representation, the robots can estimate the categories and positions of the objects, and can detect the suitable grasping positions on these objects.
      The issues involved in the proposed robotic intelligent grasping study include the object classification and location, the grasping position detection and pose estimation:
      1. In the aspect of the object classification and location, the object detection method based on deep learning is adopted. We create the data set of it and use the data set to train the deep neural network. After getting the trained model, we test its performance and analyze the test results.
      2. In the aspect of the grasping position detection, the grasping position detection method based on deep learning is proposed. Similarly, we also create the data set of the grasping position detection, and use it to train another neural network and get a trained model. We compare the test results with other results generated by other methods, and analyze their effectiveness.
      3. In the aspect of the pose estimation of the grasping position, we use the coordinate transformation method to convert the two-dimensional coordinates in the images to three-dimensional coordinates under the robots, and estimate their axes’ orientations.
      Finally, we combine the deep learning based object detection, the deep learning based grasping position detection and the pose estimation of the grasping position to a visual information based intelligent grasping system of the robot in this paper. And we apply it to a real robot to do some experiments. The experimental results show that, the proposed grasping system can classify, locate and grasp objects rapidly. The proposed system can be applied to the grasping-related robotic tasks, and can be regarded as a reference for subsequent robotic researches.
Document Type学位论文
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
杜学丹. 基于视觉的机器人智能抓取系统设计与实现[D]. 哈尔滨. 哈尔滨理工大学,2018.
Files in This Item:
File Name/Size DocType Version Access License
基于视觉的机器人智能抓取系统设计与实现.(5938KB)学位论文 限制开放CC BY-NC-SA
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[杜学丹]'s Articles
Baidu academic
Similar articles in Baidu academic
[杜学丹]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[杜学丹]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.