Estimation of load side position in indirect drive robots by sensor fusion and kalman filtering

In indirect drive robot joint, discrepancies exist between the motor side and the load side due to joint flexibilities. Thus, sensor signals may not precisely represent the actual information of interest. In this paper, estimation algorithms for load side information of the indirect robot joint are investigated. Low-cost MEMS sensors, such as gyroscopes and accelerometers, are installed on the load side. Measurement dynamics are incorporated into the model to deal with the sensor noise and bias. Kalman filtering methods are designed based on the extended dynamic/kinematic model using the fusion of multiple sensor signals. Specific issue related to the noise covariance adaptation is studied. The effectiveness of the proposed schemes is experimentally demonstrated and also confirmed in the friction compensation.