An intelligent object manipulation framework for industrial tasks

Artur Saudabayev, Yerbolat Khassanov, Almas Shintemirov, Huseyin Atakan Varol

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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 languageEnglish
Title of host publication2013 IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2013
Pages1708-1713
Number of pages6
DOIs
Publication statusPublished - 2013
Event2013 10th IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2013 - Takamastu, Japan
Duration: Aug 4 2013Aug 7 2013

Other

Other2013 10th IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2013
CountryJapan
CityTakamastu
Period8/4/138/7/13

Fingerprint

End effectors
Learning algorithms
Learning systems
Robots
Range finders
Industrial robots
Computer operating systems
Object recognition
Software architecture
Manipulators
Support vector machines
Image processing
Cameras
Mathematical transformations
Lasers
Sensors

Keywords

  • LIDAR
  • Object Recognition
  • Real-Time Operating System
  • Robot Manipulation

ASJC Scopus subject areas

  • Artificial Intelligence
  • Electrical and Electronic Engineering
  • Mechanical Engineering

Cite this

Saudabayev, A., Khassanov, Y., Shintemirov, A., & Varol, H. A. (2013). An intelligent object manipulation framework for industrial tasks. In 2013 IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2013 (pp. 1708-1713). [6618173] https://doi.org/10.1109/ICMA.2013.6618173

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 proceedingConference contribution

Saudabayev, A, Khassanov, Y, Shintemirov, A & Varol, HA 2013, An intelligent object manipulation framework for industrial tasks. in 2013 IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2013., 6618173, pp. 1708-1713, 2013 10th IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2013, Takamastu, Japan, 8/4/13. https://doi.org/10.1109/ICMA.2013.6618173
Saudabayev A, Khassanov Y, Shintemirov A, Varol HA. An intelligent object manipulation framework for industrial tasks. In 2013 IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2013. 2013. p. 1708-1713. 6618173 https://doi.org/10.1109/ICMA.2013.6618173
Saudabayev, Artur ; Khassanov, Yerbolat ; Shintemirov, Almas ; Varol, Huseyin Atakan. / An intelligent object manipulation framework for industrial tasks. 2013 IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2013. 2013. pp. 1708-1713
@inproceedings{f041480f668647d0b01fdb2dfe20aa90,
title = "An intelligent object manipulation framework for industrial tasks",
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.",
keywords = "LIDAR, Object Recognition, Real-Time Operating System, Robot Manipulation",
author = "Artur Saudabayev and Yerbolat Khassanov and Almas Shintemirov and Varol, {Huseyin Atakan}",
year = "2013",
doi = "10.1109/ICMA.2013.6618173",
language = "English",
isbn = "9781467355582",
pages = "1708--1713",
booktitle = "2013 IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2013",

}

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

SN - 9781467355582

SP - 1708

EP - 1713

BT - 2013 IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2013

ER -