Universal approximation using radial-basis-function networks
Neural Computation
International Journal of Robotics Research
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Curve and Surface Interpolation Using Rational Radial Basis Functions
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
Prediction of geometric errors of robot manipulators with Particle Swarm Optimisation method
Robotics and Autonomous Systems
Parametric design optimization of 2-DOF R-R planar manipulator-A design of experiment approach
Robotics and Computer-Integrated Manufacturing
Absolute calibration of an ABB IRB 1600 robot using a laser tracker
Robotics and Computer-Integrated Manufacturing
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Inaccurate positioning of the robot end effector causes joint deformation as well as geometric errors when an industrial robot has a payload at its end effector. We propose a new approach of calibration which deals with joint angle dependent errors to compensate for these phenomena. To implement this method, we divided the robot workspace into several local regions, and built a calibration equation by generating the constraint conditions of the end effector's motion in each local region using a three-dimensional position measurement system. The parameter errors obtained this way were interpolated using the Radial Basis Function Network (RBFN) so as to estimate calibration errors in the regions that we did not measure. We used this technique to improve the performance of a six DOF industrial robot used for arc welding.