Using statistical linearization in experiment design for identification of robotic manipulators

Abstract

It is shown how nonlinear joint stiffness in industrial robots can be determined quickly and accurately through a combination of statistical linearization and optimized data acquisition configurations. The statistical linearization is carried out using the histogram of the measured motor torques. The result of this linearization is used in a criterion that is minimized to determine optimal configurations for data collection. The proposed approach is validated using data from both simulations and experiments with a medium-size industrial robot. In both cases, there is a significant improvement in accuracy compared to both using conventional linearization and collecting data in a larger but random set of configurations. • Higher robot model accuracy using nonlinear transmission stiffnesses. • Experiment design algorithm improved by including statistical linearization. • Informative robot configurations are optimally selected from a set of candidates. • Benefits of experiment design and statistical linearization are shown by experiments.