Investigation of Using ANN and Stereovision in Delta Robot for Pick and Place Applications

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dc.contributor.author Youssef, Abdelrahman
dc.contributor.author Bayoumy, Amgad M
dc.contributor.author Atia, Mostafa R.A
dc.date.accessioned 2021-11-12T17:26:41Z
dc.date.available 2021-11-12T17:26:41Z
dc.date.issued 05/10/2021
dc.identifier.other https://doi.org/10.18280/mmep.080502
dc.identifier.uri https://bit.ly/31XSmzV
dc.description.abstract Although the Delta robot has many applications in pick-and-place operations, some problems limit its use. The difficult programming is one of these limitations. Parallel robot programming depends on solving the forward and inverse kinematics equations of the robot. These equations relate the geometric parameters, such as length and angle of every robot arm, to the position of the end effector and vice versa. The kinematic equations are hard to be derived. Moreover, any change in the robot geometry, due to a change in the application condition, requires that new kinematic equations be derived. This needs a very sophisticated and specialized programmer, who is not always available. Consequently, this problem limits the use of parallel robots. This paper discusses the use of ANN and embedded systems in addition to stereo vision to command delta robot in pick-and-place operations. The method is implemented and tested giving satisfactory results. en_US
dc.description.uri https://www.scimagojr.com/journalsearch.php?q=21100863633&tip=sid&clean=0
dc.language.iso en_US en_US
dc.publisher IIETA en_US
dc.relation.ispartofseries Mathematical Modelling of Engineering Problems;Vol. 8, No. 5, October, 2021, pp. 682-688
dc.subject parallel robot en_US
dc.subject delta robot en_US
dc.subject neural networks en_US
dc.subject artificial intelligence en_US
dc.subject pick and place en_US
dc.subject forward kinematics en_US
dc.subject inverse kinematics en_US
dc.title Investigation of Using ANN and Stereovision in Delta Robot for Pick and Place Applications en_US
dc.type Article en_US
dc.identifier.doi https://doi.org/10.18280/mmep.080502
dc.Affiliation October University for modern sciences and Arts (MSA) 


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