TY - JOUR

T1 - Process-integrated state estimation of optical systems with macro–micro manipulators based on wavefront filtering

AU - Schindlbeck, Christopher

AU - Pape, Christian

AU - Reithmeier, Eduard

PY - 2019/10

Y1 - 2019/10

N2 - To date, the assembly of optical systems is still not fully automated. Automated assembly can be facilitated by having an optical simulation at hand, which, in turn, requires knowledge about the current state of the optical system. Due to the strict demands on the positioning tolerances, the uncertainties of the positioning system play an important role and lead to non-negligible deviations from the nominal poses. Therefore, the actual poses of the optical components need to be estimated in order to correct misaligned components. Furthermore, it is beneficial to develop methods that utilize the dedicated primary sensor and to avoid additional external sensors. In this letter, we employ a macro–micro manipulator for moving optical components and utilize filtering methods for realizing an in-process state estimation. For this, the uncertainty of the positioning system, as well as sensor noise, need to be identified, which lay the groundwork for methods such as the extended Kalman filter, iterated extended Kalman filter, unscented Kalman filter, or particle filter. In this letter, we compare these methods in simulation with current nonlinear approaches from the literature with respect to the estimation error. Experimental verification is carried out by a macro–micro manipulator comprised of a Cartesian piezo-driven 3-DOF positioning system attached to a 6-DOF industrial robot. With the proposed filtering approach and macro–micro manipulator, the pose of a bi-convex lens is estimated via a wavefront sensor.

AB - To date, the assembly of optical systems is still not fully automated. Automated assembly can be facilitated by having an optical simulation at hand, which, in turn, requires knowledge about the current state of the optical system. Due to the strict demands on the positioning tolerances, the uncertainties of the positioning system play an important role and lead to non-negligible deviations from the nominal poses. Therefore, the actual poses of the optical components need to be estimated in order to correct misaligned components. Furthermore, it is beneficial to develop methods that utilize the dedicated primary sensor and to avoid additional external sensors. In this letter, we employ a macro–micro manipulator for moving optical components and utilize filtering methods for realizing an in-process state estimation. For this, the uncertainty of the positioning system, as well as sensor noise, need to be identified, which lay the groundwork for methods such as the extended Kalman filter, iterated extended Kalman filter, unscented Kalman filter, or particle filter. In this letter, we compare these methods in simulation with current nonlinear approaches from the literature with respect to the estimation error. Experimental verification is carried out by a macro–micro manipulator comprised of a Cartesian piezo-driven 3-DOF positioning system attached to a 6-DOF industrial robot. With the proposed filtering approach and macro–micro manipulator, the pose of a bi-convex lens is estimated via a wavefront sensor.

KW - Calibration and identification

KW - Industrial robots

KW - Micro/nanorobots

KW - Redundant robots

UR - http://www.scopus.com/inward/record.url?scp=85076469662&partnerID=8YFLogxK

U2 - 10.1109/LRA.2019.2930423

DO - 10.1109/LRA.2019.2930423

M3 - Article

AN - SCOPUS:85076469662

VL - 4

SP - 4078

EP - 4085

JO - IEEE Robotics and Automation Letters

JF - IEEE Robotics and Automation Letters

SN - 2377-3766

IS - 4

ER -