Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.1/2249
Título: Neural network approach to collision free path-planning for robotic manipulators
Autor: Pashkevich, A.
Kazheunikau, M.
Ruano, A. E.
Palavras-chave: Robotic manipulators
Configuration space
Neural networks
Off-line programming
Data: 2006
Editora: Taylor & Francis
Citação: Pashkevich, A.; Kazheunikau, M.; Ruano, A. E. Neural network approach to collision free path-planning for robotic manipulators, International Journal of Systems Science, 37, 8, 555-564, 2006.
Resumo: The paper deals with collision free path-planning for industrial robotic manipulators A new efficient approach is proposed that is based on the topologically ordered neural network model. This model describes harmonic potential field of the robot configuration space, sampled by the non-regular grid. The developed path-planning algorithm takes into account highly-irregular shape of the obstacles of welding and assembling robotic cells, and provides reduced number of collision checking. The stability of the topologically ordered neural network is investigated. The algorithm has been successfully applied to the off-line programming of a robotic manufacturing cell for the automotive industry.
Peer review: yes
URI: http://hdl.handle.net/10400.1/2249
ISSN: 0020-7721
Aparece nas colecções:FCT2-Artigos (em revistas ou actas indexadas)

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