Tool Position Estimation of a Flexible Industrial Robot using Recursive Bayesian Methods
2011 (English)Report (Other academic)
A sensor fusion method for state estimation of a flexible industrial robot is presented. By measuring the acceleration at the end-effector, the accuracy of the arm angular position is improved significantly when these measurements are fused with motor angle observation. The problem is formulated in a Bayesian estimation framework and two solutions are proposed; one using the extended Kalman filter (EKF) and one using the particle filter (PF). The technique is verified on experiments on the ABB IRB4600 robot, where the accelerometer method is showing a significant better dynamic performance, even when model errors are present.
Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2011. , 6 p.
LiTH-ISY-R, ISSN 1400-3902 ; 3024
Estimation, Extended Kalman Filter, Particle Filter, Accelerometer, Industrial Robot
IdentifiersURN: urn:nbn:se:liu:diva-88972ISRN: LiTH-ISY-R-3024OAI: oai:DiVA.org:liu-88972DiVA: diva2:606579
ProjectsVinnova Excellence Center LINK-SICSSF project Collaborative Localization
FunderVinnovaSwedish Foundation for Strategic Research