Configuration space synthesis for robotic manipulators using neural networks

The paper deals with configuration space syntheses for industrial robotic manipulators. A new efficient method is proposed that is based on a neural network collision model. To generate the collision model, a modification of the Radial Basis Function Network (RBFN) is used, which is trained applying...

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Bibliographic Details
Main Author: Pashkevich, A. (author)
Other Authors: Ruano, Antonio (author), Kazheunikau, M. (author)
Format: conferenceObject
Language:eng
Published: 2013
Subjects:
Robotic manipulators
Configuration space
Neural network
Learning algorithms
Off-line programming
Radial base function network
Online Access:http://hdl.handle.net/10400.1/2304
Country:Portugal
Oai:oai:sapientia.ualg.pt:10400.1/2304
Description
Summary:The paper deals with configuration space syntheses for industrial robotic manipulators. A new efficient method is proposed that is based on a neural network collision model. To generate the collision model, a modification of the Radial Basis Function Network (RBFN) is used, which is trained applying the developed algorithm. An obstacle transformation algorithm that is based on conjugate vector model of a robotic cell is proposed. The method has been successfully applied to the design of a robotic manufacturing cell for the automotive industry.

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