Details
| Original language | English |
|---|---|
| Pages (from-to) | 4078-4085 |
| Number of pages | 8 |
| Journal | IEEE Robotics and Automation Letters |
| Volume | 4 |
| Issue number | 4 |
| Early online date | 23 Jul 2019 |
| Publication status | Published - Oct 2019 |
Abstract
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.
Keywords
- Calibration and identification, Industrial robots, Micro/nanorobots, Redundant robots
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Engineering(all)
- Biomedical Engineering
- Computer Science(all)
- Human-Computer Interaction
- Engineering(all)
- Mechanical Engineering
- Computer Science(all)
- Computer Vision and Pattern Recognition
- Computer Science(all)
- Computer Science Applications
- Mathematics(all)
- Control and Optimization
- Computer Science(all)
- Artificial Intelligence
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In: IEEE Robotics and Automation Letters, Vol. 4, No. 4, 10.2019, p. 4078-4085.
Research output: Contribution to journal › Article › Research › peer review
}
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 -