Authors:
Hamed Mesgari
;
Farzad Cheraghpour
and
S. Ali A. Moosavian
Affiliation:
K.N. Toosi University of Technology, Iran, Islamic Republic of
Keyword(s):
Optimization, Performance index, Object manipulation, Robotic manipulator, Grasping, Particle Swarm Optimization, Genetic Algorithm.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Modeling, Simulation and Architectures
;
Optimization Algorithms
;
Robotics and Automation
;
Soft Computing
Abstract:
Grasp planning is one of the most interesting subjects of object manipulation tasks in robotics and the development of grasp methods would be affected the robot performance. One of the most important subjects which is discussed in grasp planning, especially in industrial applications, is optimal grasp planning and finding the best grasping point. So it is important to find the best grasping point that the manipulator contact with object. In this paper, the MAG performance index, which is designed for object manipulation tasks, would be used for two different types of objects which are manipulated in the predefined path. Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) methods would be used to maximize this index and find the best grasping point and finally compared with each other. The results show that in faster object manipulation tasks, the GA method is more suitable than PSO method. Since in accurate object manipulation tasks, the PSO method is preferred to GA method.