Performance quantification of observer-based robot impact detection under modeling uncertainties

Abstract

This work proposes a design and performance quantification methodology for observer-based impact detection in serial robot manipulators in presence of modelling errors and without force/torque sensor. After expressing the modelling errors between the physical robot and its inverse dynamic model as the sum of contributions due to dynamic parameters uncertainties and numerical differentiation errors for a given trajectory, an observer of the external disturbance torque is designed based on the inverse dynamic model and using a Kalman filter. The influence of each design parameter of the observer on the quality of the external torque estimation is studied first based on simulation results. Then a frequency analysis is conducted to distinguish between the influence of the exact external torque, the modelling uncertainties and the measurement noise on the estimated external torque. Finally a methodology is proposed to determine the optimal design corresponding to the shortest detection time depending on the expected sensitivity.