Wavelet networks for estimation of coupled friction in robotic manipulators

Abstract

A wavelet network (WN) friction model has been developed for robots where the friction is coupled, such that it is a function of the velocity of multiple joints. Wavelets have the ability to estimate random friction maps without any prior modeling while preserving linearity in the model parameters. The WN friction model was compared against the Coulomb+viscous (CV) model through experiments with the PHANTOM Omni haptic device (SensAble Technologies, MA, USA); however, the theory is valid for any serial-chain robotic manipulator. Ability to estimate applied motor torques was used as the performance metric, quantified using relative RMS values. During training of the WN model it outperformed the CV model in all cases, with an improvement in relative RMS ranging from 0.4 to 7.5 percentage points, illustrating the potential of the WN friction model. However, during testing of the WN model on an independent data set results were mixed, highlighting the challenge of achieving sufficient training.