An improved approach to the solution of inverse kinematics problems for robot manipulators

dc.contributor.authorKarlik, B
dc.contributor.authorAydin, S
dc.date.accessioned2025-04-10T10:25:47Z
dc.date.available2025-04-10T10:25:47Z
dc.description.abstractA structured artificial neural-network (ANN) approach has been proposed here to control the motion of a robot manipulator. Many neural-network models use threshold units with sigmoid transfer functions and gradient descent-type learning rules. The learning equations used are those of the backpropagation algorithm. In this work, the solution of the kinematics of a six-degrees-of-freedom robot manipulator is implemented by using ANN. Work has been undertaken to find the best ANN configurations for this problem. Both the placement and orientation angles of a robot manipulator are used to fin the inverse kinematics solutions. (C) 2000 Elsevier Science Ltd. All rights reserved.
dc.identifier.issn0952-1976
dc.identifier.urihttp://hdl.handle.net/20.500.14701/33536
dc.language.isoEnglish
dc.titleAn improved approach to the solution of inverse kinematics problems for robot manipulators
dc.typeArticle

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