Bayesian position estimation of an industrial robot using multiple sensors

A modern industrial robot control system is often only based upon measurements from the motors of the manipulator. To perform good trajectory tracking on the arm side of the robot a very accurate description of the system must therefore be used. A sensor fusion technique is presented to achieve good estimates of the position of the robot using a very simple model. By using information from an accelerometer at the tool of the robot the effect of unmodelled dynamics can be measured. The estimate of the tool position can be improved to enhance accuracy. We formulate the computation of the position as a Bayesian estimation problem and propose two solutions. The first solution uses the extended Kalman filter (EKF) as a fast but linearized estimator. The second uses the particle filter, which can solve the Bayesian estimation problem without linearizations or any Gaussian noise assumptions. Since the aim is to use the positions estimates to improve position with an iterative learning control method, no computational constraints arise. The methods are applied to experimental data from an ABB IRB1400 commercial industrial robot and to data from a simulation of a realistic flexible robot model, showing a significant improvement in position accuracy.