An improved approach to the solution of inverse kinematics problems for robot manipulators
dc.contributor.author | Karlik, B | |
dc.contributor.author | Aydin, S | |
dc.date.accessioned | 2025-04-10T10:25:47Z | |
dc.date.available | 2025-04-10T10:25:47Z | |
dc.description.abstract | A 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.issn | 0952-1976 | |
dc.identifier.uri | http://hdl.handle.net/20.500.14701/33536 | |
dc.language.iso | English | |
dc.title | An improved approach to the solution of inverse kinematics problems for robot manipulators | |
dc.type | Article |