SDI: A sparse drift identification approach for force/torque sensor calibration in industrial robots

Xuechun Qiao, Chenxuan Xu, Yasen Wang, Guijun Ma

Abstract: Calibrating force/torque (F/T) sensor drift is an enduring objective for robotic precise force control. This article presents a novel drift identification method to discover the dynamics of F/T sensor drifts from noisy measurement data, which is conducive to accurate sensor drift compensation. In the drift identification method, a linear dynamical model with measurement noise is formulated to characterize the evolution of sensor drift, and an expectation–maximization optimization framework which integrates Kalman smoothing with sparse Bayesian learning is put forward to identify the parameters of the linear dynamical model using F/T sensor measurement data. The effectiveness of the proposed drift identification method is validated on extensive robotic experiments including scenarios with unloaded mass, loaded mass, and contact force. Experimental results demonstrate the superiority of the proposed drift identification method compared to the conventional least square method for sensor calibration.