Intelligent robotic grasping and manipulation system with deep learning
Random object grasping is a crucial problem in robotics which is yet to be solved. Typically, vision-based robotic grasping can be classified into two approaches, 2D planar grasp and 6-DoF (degree of freedom) grasp. In this project, the focus will be on the prediction of 6-DoF grasp poses based o...
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sg-ntu-dr.10356-1580292023-07-07T19:29:48Z Intelligent robotic grasping and manipulation system with deep learning Chu, You-Rui Lin Zhiping School of Electrical and Electronic Engineering Singapore Institute of Manufacturing Technology Zhu Haiyue EZPLin@ntu.edu.sg, zhu_haiyue@simtech.a-star.edu.sg Engineering::Electrical and electronic engineering Random object grasping is a crucial problem in robotics which is yet to be solved. Typically, vision-based robotic grasping can be classified into two approaches, 2D planar grasp and 6-DoF (degree of freedom) grasp. In this project, the focus will be on the prediction of 6-DoF grasp poses based on RGB-D images. Most of the current approaches for 6-DoF grasp are generated from point clouds or unstable depth images, which may lead to undesirable results in some cases. The proposed method divides the 6-DoF grasp detection into three sub-stages. The first stage is the LocNet, a convolutional-based encoder-decoder neural network to predict the location of the objects in the image. Besides, ViewAngleNet is also a convolutional-based encoder-decoder neural network that predicts the 3D rotation groups of the gripper at the image location of the objects, similar to LocNet but with a different output head. Afterwards, an analytical search algorithm will determine the gripper's opening width and the gripper’s distance from the grasp point. Real-world experiments are conducted with a UR10 robot arm, an Intel Realsense camera and a Robotiq two-finger gripper on single-object scenes and cluttered scenes, which show satisfactory success rates. Bachelor of Engineering (Electrical and Electronic Engineering) 2022-05-27T01:45:21Z 2022-05-27T01:45:21Z 2022 Final Year Project (FYP) Chu, Y. (2022). Intelligent robotic grasping and manipulation system with deep learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158029 https://hdl.handle.net/10356/158029 en application/pdf Nanyang Technological University |
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Engineering::Electrical and electronic engineering Chu, You-Rui Intelligent robotic grasping and manipulation system with deep learning |
description |
Random object grasping is a crucial problem in robotics which is yet to be solved.
Typically, vision-based robotic grasping can be classified into two approaches, 2D planar
grasp and 6-DoF (degree of freedom) grasp. In this project, the focus will be on the
prediction of 6-DoF grasp poses based on RGB-D images. Most of the current
approaches for 6-DoF grasp are generated from point clouds or unstable depth images,
which may lead to undesirable results in some cases. The proposed method divides the
6-DoF grasp detection into three sub-stages. The first stage is the LocNet, a
convolutional-based encoder-decoder neural network to predict the location of the
objects in the image. Besides, ViewAngleNet is also a convolutional-based
encoder-decoder neural network that predicts the 3D rotation groups of the gripper at the
image location of the objects, similar to LocNet but with a different output head.
Afterwards, an analytical search algorithm will determine the gripper's opening width
and the gripper’s distance from the grasp point.
Real-world experiments are conducted with a UR10 robot arm, an Intel Realsense
camera and a Robotiq two-finger gripper on single-object scenes and cluttered scenes,
which show satisfactory success rates. |
author2 |
Lin Zhiping |
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Lin Zhiping Chu, You-Rui |
format |
Final Year Project |
author |
Chu, You-Rui |
author_sort |
Chu, You-Rui |
title |
Intelligent robotic grasping and manipulation system with deep learning |
title_short |
Intelligent robotic grasping and manipulation system with deep learning |
title_full |
Intelligent robotic grasping and manipulation system with deep learning |
title_fullStr |
Intelligent robotic grasping and manipulation system with deep learning |
title_full_unstemmed |
Intelligent robotic grasping and manipulation system with deep learning |
title_sort |
intelligent robotic grasping and manipulation system with deep learning |
publisher |
Nanyang Technological University |
publishDate |
2022 |
url |
https://hdl.handle.net/10356/158029 |
_version_ |
1772825280928284672 |