Robotized task time scheduling and optimization based on Genetic Algorithms for non redundant industrial manipulators

Khelifa Baizid; Amal Meddahi; Ali Yousnadj; Ryad Chellali; Hamza Khan; Jamshed Iqbal

doi:10.1109/ROSE.2014.6953033

Robotized task time scheduling and optimization based on Genetic Algorithms for non redundant industrial manipulators

Khelifa Baizid, Amal Meddahi, Ali Yousnadj, Ryad Chellali, Hamza Khan, Jamshed Iqbal

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

9 Citations (Scopus)

Abstract

Industrial robot manipulators must work as fast as possible in order to increase the productivity. This goal could be achieved by increasing robots speed or/and optimizing the trajectories followed by robots while performing assembly, welding or similar tasks. In our contribution, we focus on the second aspect and we target the shortening of paths between task-points. In other words, the goal is to find the shorter traveled distance between different configurations in the coordinate space. In addition to the short distance goal, we aim as well to impose both IKM (Inverse Kinematic Model) and the relative position and orientation of the manipulator regarding the task-points. To this end, we propose an optimization method based on Genetics Algorithms. The method is validated via numerical and graphical simulation, where, results show that the total cycle time required to perform a spot-welding task of an industrial car-body by a 6-DOFs (Degree Of Freedoms) industrial manipulator was drastically reduced.

Original language	English
Title of host publication	ROSE 2014 - 2014 IEEE International Symposium on RObotic and SEnsors Environments, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	112-117
Number of pages	6
ISBN (Electronic)	9781479949274
DOIs	https://doi.org/10.1109/ROSE.2014.6953033
Publication status	Published - 1 Jan 2014
Event	2014 12th IEEE International Symposium on Robotic and Sensors Environments, ROSE 2014 - Timisoara, Romania Duration: 16 Oct 2014 → 18 Oct 2014

Conference

Conference	2014 12th IEEE International Symposium on Robotic and Sensors Environments, ROSE 2014
Country/Territory	Romania
City	Timisoara
Period	16/10/14 → 18/10/14

Keywords

genetic algorithms
Industrial manipulator
optimization

ASJC Scopus subject areas

Artificial Intelligence
Human-Computer Interaction
Electrical and Electronic Engineering

Access to Document

10.1109/ROSE.2014.6953033

Cite this

Baizid, K., Meddahi, A., Yousnadj, A., Chellali, R., Khan, H., & Iqbal, J. (2014). Robotized task time scheduling and optimization based on Genetic Algorithms for non redundant industrial manipulators. In ROSE 2014 - 2014 IEEE International Symposium on RObotic and SEnsors Environments, Proceedings (pp. 112-117). Article 6953033 Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ROSE.2014.6953033

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title = "Robotized task time scheduling and optimization based on Genetic Algorithms for non redundant industrial manipulators",

abstract = "Industrial robot manipulators must work as fast as possible in order to increase the productivity. This goal could be achieved by increasing robots speed or/and optimizing the trajectories followed by robots while performing assembly, welding or similar tasks. In our contribution, we focus on the second aspect and we target the shortening of paths between task-points. In other words, the goal is to find the shorter traveled distance between different configurations in the coordinate space. In addition to the short distance goal, we aim as well to impose both IKM (Inverse Kinematic Model) and the relative position and orientation of the manipulator regarding the task-points. To this end, we propose an optimization method based on Genetics Algorithms. The method is validated via numerical and graphical simulation, where, results show that the total cycle time required to perform a spot-welding task of an industrial car-body by a 6-DOFs (Degree Of Freedoms) industrial manipulator was drastically reduced.",

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author = "Khelifa Baizid and Amal Meddahi and Ali Yousnadj and Ryad Chellali and Hamza Khan and Jamshed Iqbal",

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Baizid, K, Meddahi, A, Yousnadj, A, Chellali, R, Khan, H & Iqbal, J 2014, Robotized task time scheduling and optimization based on Genetic Algorithms for non redundant industrial manipulators. in ROSE 2014 - 2014 IEEE International Symposium on RObotic and SEnsors Environments, Proceedings., 6953033, Institute of Electrical and Electronics Engineers Inc., pp. 112-117, 2014 12th IEEE International Symposium on Robotic and Sensors Environments, ROSE 2014, Timisoara, Romania, 16/10/14. https://doi.org/10.1109/ROSE.2014.6953033

Robotized task time scheduling and optimization based on Genetic Algorithms for non redundant industrial manipulators. / Baizid, Khelifa; Meddahi, Amal; Yousnadj, Ali et al.
ROSE 2014 - 2014 IEEE International Symposium on RObotic and SEnsors Environments, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2014. p. 112-117 6953033.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Robotized task time scheduling and optimization based on Genetic Algorithms for non redundant industrial manipulators

AU - Baizid, Khelifa

AU - Meddahi, Amal

AU - Yousnadj, Ali

AU - Chellali, Ryad

AU - Khan, Hamza

AU - Iqbal, Jamshed

PY - 2014/1/1

Y1 - 2014/1/1

N2 - Industrial robot manipulators must work as fast as possible in order to increase the productivity. This goal could be achieved by increasing robots speed or/and optimizing the trajectories followed by robots while performing assembly, welding or similar tasks. In our contribution, we focus on the second aspect and we target the shortening of paths between task-points. In other words, the goal is to find the shorter traveled distance between different configurations in the coordinate space. In addition to the short distance goal, we aim as well to impose both IKM (Inverse Kinematic Model) and the relative position and orientation of the manipulator regarding the task-points. To this end, we propose an optimization method based on Genetics Algorithms. The method is validated via numerical and graphical simulation, where, results show that the total cycle time required to perform a spot-welding task of an industrial car-body by a 6-DOFs (Degree Of Freedoms) industrial manipulator was drastically reduced.

AB - Industrial robot manipulators must work as fast as possible in order to increase the productivity. This goal could be achieved by increasing robots speed or/and optimizing the trajectories followed by robots while performing assembly, welding or similar tasks. In our contribution, we focus on the second aspect and we target the shortening of paths between task-points. In other words, the goal is to find the shorter traveled distance between different configurations in the coordinate space. In addition to the short distance goal, we aim as well to impose both IKM (Inverse Kinematic Model) and the relative position and orientation of the manipulator regarding the task-points. To this end, we propose an optimization method based on Genetics Algorithms. The method is validated via numerical and graphical simulation, where, results show that the total cycle time required to perform a spot-welding task of an industrial car-body by a 6-DOFs (Degree Of Freedoms) industrial manipulator was drastically reduced.

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Baizid K, Meddahi A, Yousnadj A, Chellali R, Khan H, Iqbal J. Robotized task time scheduling and optimization based on Genetic Algorithms for non redundant industrial manipulators. In ROSE 2014 - 2014 IEEE International Symposium on RObotic and SEnsors Environments, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2014. p. 112-117. 6953033 doi: 10.1109/ROSE.2014.6953033

Robotized task time scheduling and optimization based on Genetic Algorithms for non redundant industrial manipulators

Abstract

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Keywords

ASJC Scopus subject areas

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