Bayesian position estimation of an industrial robot using multiple sensors

Abstract

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. In the paper 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.Keywords: extended kalman filter, particle filter, robotics, accelerometer Bayesian Position Estimation of an Industrial Robot using Multiple Sensors Rickard Karlsson and Mikael NorrlöfAbstract-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. In the paper 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.

Tool position estimation of a flexible industrial robot using recursive bayesian methods

“…The position root mean square error (RMSE) is presented in Fig 6, where the EKF acceleration method shows a significantly better performance. This is based on the single experimental trajectory, but the result is in accordance with previous simulation result as well as theoretical calculations in [15], [16]. The MATLAB implementation of the EKF is almost real-time, and without losing performance the measurements can be slightly decimated (to approximately 200 Hz), yielding faster than real-time calculations.…”

“…particle filtering based on external sensors have so far been applied to very few industrial robotic applications, [15], [16], [21], focusing on evaluation and simulation. This paper extends the idea to experimental data, from a state of the art industrial robot, for evaluation of the EKF and the PF.…”

Position Estimation and Modeling of a Flexible Industrial Robot

“…The PF method is also motivated since it provides the possibility design control laws and perform diagnosis in a much more advanced way. This paper extends the idea introduced in (Karlsson and Norrlöf, 2004), where experimental data was used in an EKF together with tool acceleration measurements. Here, a performance evaluation in a simulation environment is presented for both the EKF and the particle filter.…”

Bayesian state estimation of a flexible industrial robot

“…The PF method is also motivated since it provides the possibility to design control laws and perform diagnosis in a much more advanced way. This paper extends the idea introduced in [18]. A performance evaluation in a simulation environment for both the EKF and the PF is presented and it is extensively analyzed using the Cramér-Rao lower bound (CRLB) [4], [19].…”