Abstract
This paper presents an intelligent object manipulation framework for industrial tasks, which integrates a sensor-rich multi-fingered robot hand, an industrial robot manipulator, a conveyor belt and employs machine learning algorithms. The framework software architecture is implemented using a Windows 7 operating system with RTX real-time extension for synchronous handling of peripheral devices. The framework uses Scale Invariant Feature Transform (SIFT) image processing algorithm, Support Vector Machine (SVM) machine learning algorithm and 3D point cloud techniques for intelligent object recognition based on RGB camera and laser rangefinder information from the robot hand end effector. The objective is automated manipulation of objects with different shapes and poses with minimum programming effort applied by a user.
| Original language | English |
|---|---|
| Title of host publication | 2013 IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2013 |
| Pages | 1708-1713 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 2013 |
| Event | 2013 10th IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2013 - Takamastu, Japan Duration: Aug 4 2013 → Aug 7 2013 |
Other
| Other | 2013 10th IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2013 |
|---|---|
| Country | Japan |
| City | Takamastu |
| Period | 8/4/13 → 8/7/13 |
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Keywords
- LIDAR
- Object Recognition
- Real-Time Operating System
- Robot Manipulation
ASJC Scopus subject areas
- Artificial Intelligence
- Electrical and Electronic Engineering
- Mechanical Engineering
Cite this
An intelligent object manipulation framework for industrial tasks. / Saudabayev, Artur; Khassanov, Yerbolat; Shintemirov, Almas; Varol, Huseyin Atakan.
2013 IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2013. 2013. p. 1708-1713 6618173.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - An intelligent object manipulation framework for industrial tasks
AU - Saudabayev, Artur
AU - Khassanov, Yerbolat
AU - Shintemirov, Almas
AU - Varol, Huseyin Atakan
PY - 2013
Y1 - 2013
N2 - This paper presents an intelligent object manipulation framework for industrial tasks, which integrates a sensor-rich multi-fingered robot hand, an industrial robot manipulator, a conveyor belt and employs machine learning algorithms. The framework software architecture is implemented using a Windows 7 operating system with RTX real-time extension for synchronous handling of peripheral devices. The framework uses Scale Invariant Feature Transform (SIFT) image processing algorithm, Support Vector Machine (SVM) machine learning algorithm and 3D point cloud techniques for intelligent object recognition based on RGB camera and laser rangefinder information from the robot hand end effector. The objective is automated manipulation of objects with different shapes and poses with minimum programming effort applied by a user.
AB - This paper presents an intelligent object manipulation framework for industrial tasks, which integrates a sensor-rich multi-fingered robot hand, an industrial robot manipulator, a conveyor belt and employs machine learning algorithms. The framework software architecture is implemented using a Windows 7 operating system with RTX real-time extension for synchronous handling of peripheral devices. The framework uses Scale Invariant Feature Transform (SIFT) image processing algorithm, Support Vector Machine (SVM) machine learning algorithm and 3D point cloud techniques for intelligent object recognition based on RGB camera and laser rangefinder information from the robot hand end effector. The objective is automated manipulation of objects with different shapes and poses with minimum programming effort applied by a user.
KW - LIDAR
KW - Object Recognition
KW - Real-Time Operating System
KW - Robot Manipulation
UR - http://www.scopus.com/inward/record.url?scp=84887984987&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84887984987&partnerID=8YFLogxK
U2 - 10.1109/ICMA.2013.6618173
DO - 10.1109/ICMA.2013.6618173
M3 - Conference contribution
AN - SCOPUS:84887984987
SN - 9781467355582
SP - 1708
EP - 1713
BT - 2013 IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2013
ER -