Vision Guided Servoing using Neural Networks - Université de Bretagne Occidentale Access content directly
Conference Papers Year : 1996

Vision Guided Servoing using Neural Networks


In a closed loop control system, a six-degree-of-freedom robot with a CCD-camera mounted on its end-effector, is operated. An object moves freely in 3D space(translation + rotation). The aim is to servo the camera on the object, so that the image of the object is always close to the reference image", defined by a given reference position of the object with respect to the camera. Classical kinematics equations are first studied in order to determine the significant parameters of the problem. Two neural approaches are then proposed: in the first solution, a Multi-Layer Perceptron (MLP) is fed with the image coordinates of feature points and with previous robot commands. In the second solution, different neural input parameters are used, that are based on affine transformations between succeeding image coordinates. Results and comparisons with the classical approach (uniform and non-uniform translations of the object) are presented, and show the interest of the approach vs. classical methods.
Fichier principal
Vignette du fichier
1996_07_cesa_robot.pdf (516.44 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-03222628 , version 1 (18-03-2023)




  • HAL Id : hal-03222628 , version 1


Nadine Rondel, Gilles Burel. Vision Guided Servoing using Neural Networks. International Conference on Computational Engineering in Systems Applications (CESA96), IEEE-SMC (Institute of electrical and electronics engineers, Systems, man, and cybernetics society) & IMACS (International Symposium on Iterative Methods in Scientific Computing), Jul 1996, Lille, France. pp.128-133. ⟨hal-03222628⟩


25 View
6 Download


Gmail Mastodon Facebook X LinkedIn More