Learning based robotic grasping
Nowadays, with the rapid development of artificial intelligence, machine learning has made great strides in making computers behave more intelligently. In this context, machine learning has been applied to robots to make them work in a more reasonable way. This has enabled rapid advances in robotic...
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| Format: | Thesis-Master by Coursework |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/158933 |
| _version_ | 1826121977639731200 |
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| author | Wang, Chongyu |
| author2 | Cheah Chien Chern |
| author_facet | Cheah Chien Chern Wang, Chongyu |
| author_sort | Wang, Chongyu |
| collection | NTU |
| description | Nowadays, with the rapid development of artificial intelligence, machine learning has made great strides in making computers behave more intelligently. In this context, machine learning has been applied to robots to make them work in a more reasonable way. This has enabled rapid advances in robotic work planning.
At the same time, with the transformation and upgrading of manufacturing, intelligent industrial robots with vision systems are widely used in modern factories. In order to deploy robots rapidly in flexible manufacturing and make robots operate objects accurately, this dissertation studied a robot that applies machine learning to robot object detecting.
The content and results of this report mainly include the following aspects: Firstly, this report presents a grasping robot that is able to detect objects accurately by using a model trained by YOLO (You only look once). In order to make the robot grasp objects while avoiding the sharp points on their edge, YOLO is also used to train the model to detect the sharp points on objects' edges. Training results show that the mean average precision of detecting different objects in the lab can reach as high as 91.8\%. Secondly, this report presents a method to calculate the position of the robot tool for grasping objects. The experimental results show that the robot is able to grasp the objects accurately and steadily with the detecting outcomes and robot motion commands. |
| first_indexed | 2024-10-01T05:40:58Z |
| format | Thesis-Master by Coursework |
| id | ntu-10356/158933 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:40:58Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1589332023-07-04T17:47:54Z Learning based robotic grasping Wang, Chongyu Cheah Chien Chern School of Electrical and Electronic Engineering ECCCheah@ntu.edu.sg Engineering::Electrical and electronic engineering Nowadays, with the rapid development of artificial intelligence, machine learning has made great strides in making computers behave more intelligently. In this context, machine learning has been applied to robots to make them work in a more reasonable way. This has enabled rapid advances in robotic work planning. At the same time, with the transformation and upgrading of manufacturing, intelligent industrial robots with vision systems are widely used in modern factories. In order to deploy robots rapidly in flexible manufacturing and make robots operate objects accurately, this dissertation studied a robot that applies machine learning to robot object detecting. The content and results of this report mainly include the following aspects: Firstly, this report presents a grasping robot that is able to detect objects accurately by using a model trained by YOLO (You only look once). In order to make the robot grasp objects while avoiding the sharp points on their edge, YOLO is also used to train the model to detect the sharp points on objects' edges. Training results show that the mean average precision of detecting different objects in the lab can reach as high as 91.8\%. Secondly, this report presents a method to calculate the position of the robot tool for grasping objects. The experimental results show that the robot is able to grasp the objects accurately and steadily with the detecting outcomes and robot motion commands. Master of Science (Computer Control and Automation) 2022-06-02T11:50:29Z 2022-06-02T11:50:29Z 2022 Thesis-Master by Coursework Wang, C. (2022). Learning based robotic grasping. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158933 https://hdl.handle.net/10356/158933 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Wang, Chongyu Learning based robotic grasping |
| title | Learning based robotic grasping |
| title_full | Learning based robotic grasping |
| title_fullStr | Learning based robotic grasping |
| title_full_unstemmed | Learning based robotic grasping |
| title_short | Learning based robotic grasping |
| title_sort | learning based robotic grasping |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/158933 |
| work_keys_str_mv | AT wangchongyu learningbasedroboticgrasping |