Parameter adaptive detection method of robot collisions under dynamic disturbance

Abstract

Due to variable operational loads and uncertain model parameters induced by long-time joint wear, accurate and timely collision detection remains an open issue in the field of industrial robots. It is well known that model-based methods are popular for collision detection. However, traditional model-based methods will fail to detect collision because of time-varied model parameters caused by changing joint lubrication and working load. Therefore, a dynamic parameter adaptive collision detection method is proposed to improve the accuracy and robust of identifying collision in changing dynamic environments. The modeling error and the dynamic disturbance are considered to establish the parameter updating mechanism for reducing the momentum residual. Moreover, the dynamic parameters are adaptively updated to adjust the residuals during the contact-free situations of the robot. Consequently, a smaller stable range for the residual threshold is obtained to increase the performance of collision detection. Finally, experimental measurements of 1- and 7-degree-of-freedom (DoF) robots are performed to analyze the torques and momentum residuals under different conditions. The momentum residual of the 1-DoF manipulator decreases from 25 kg∙m/s to 9 kg∙m/s, and the joint momentum residual of the 7-DoF robot reduces from 8.2 kg∙m/s to 1.6 kg∙m/s. Compared to other methods, the proposed method has the lowest threshold deviation of 0.27 kg∙m/s. The results demonstrate that the proposed method can accurately detect collision under condition of variable load and model parameters.