Sensor Fusion for Position Estimation of an Industrial Robot
2004 (English)Report (Other academic)
A modern industrial robot control system is often based only upon measurements from the motors of the manipulator. Hence to follow a trajectory with the tool an accurate description of the system must be used. In the paper a sensor fusion technique is presented to achieve good estimates of the position of the robot using a simple model. By using information from an accelerometer the effect of unmodelled dynamics can be measured. Hence, the estimate of the tool position can be improved to enhance the positioning. We formulate the computation of the position as a Bayesian estimation problem and propose two solutions. First using the extended Kalman filter EKF as a fast but linearized estimator. Second the particle filter which can solve the Bayesian estimation problem without linearizations or any Gaussian noise assumptions. Since the aim is to use the estimates to improve position accuracy using an iterative learning control method, no computational constraints arises. The methods are applied to experimental data from an ABBIRB1400 commercial industrial robot. We also discuss some preliminary results from using a detailed simulation model.
Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2004. , 9 p.
LiTH-ISY-R, ISSN 1400-3902 ; 2612
Extended kalman filter, Particle filter, Robotics, Experiment
IdentifiersURN: urn:nbn:se:liu:diva-55991ISRN: LiTH-ISY-R-2612OAI: oai:DiVA.org:liu-55991DiVA: diva2:316744