Abstract
This paper presents a new technique for the calibration of robots based on a neural network approach for the identification of non-geometrical errors. Identification of geometrical errors is not a problem any more since several methods have been presented recently. The remaining problem is the identification of the non-geometrical errors. Non-geometrical errors modeling is a very complex and heavy process. The originality of this paper is the use of a neural network approach avoiding explicit modeling of this kind of errors. Simulations have been carried out on a robot with 6 degrees of freedom. Finally, two compensation algorithms are presented, based on the improved knowledge of the model: the first one is based on the construction of false target, the second one compensates directly into the joint space.
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© 1992 Springer Science+Business Media Dordrecht
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Renders, JM., del R. Millan, J., Becquet, M. (1992). Non-Geometrical Parameters Identification for Robot Kinematic Calibration by use of Neural Network Techniques. In: Tzafestas, S.G. (eds) Robotic Systems. Microprocessor-Based and Intelligent Systems Engineering, vol 10. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-2526-0_5
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DOI: https://doi.org/10.1007/978-94-011-2526-0_5
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-010-5115-6
Online ISBN: 978-94-011-2526-0
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