Configuration space synthesis for robotic manipulators using neural networks

Pashkevich, A.; Ruano, Antonio; Kazheunikau, M.

http://hdl.handle.net/10400.1/2304

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Authors

Pashkevich, A.

Ruano, Antonio

Kazheunikau, M.

Abstract(s)

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.

Keywords

Robotic manipulators Configuration space Neural network Learning algorithms Off-line programming Radial base function network

URI

http://hdl.handle.net/10400.1/2304

Citation

Pashkevich, A.; Ruano, A. E.; Kazheunikau, M. Configuration Space Synthesis for Robotic Manipulators using Neural Networks, Trabalho apresentado em 5th Portuguese Conference on Automatic Control (Controlo 2002), In 5th Portuguese Conference on Automatic Control (Controlo 2002), Aveiro, 2002.