Sensor Fusion for Robotic Workspace State Estimation
Olofsson, Björn LU ; Bergstedt, Jacob LU ; Kortier, Henk G. ; Bernhardsson, Bo LU
; Robertsson, Anders
LU
and Johansson, Rolf
LU
(2016)
In IEEE/ASME Transactions on Mechatronics
21(5).
p.2236-2248
- Abstract
We consider the problem of tool position and orientation state estimation for robot manipulators in workspace by sensor fusion of the internal robot joint measurements with inertial measurement unit data. A prerequisite for this to be successful is accurate calibration of the sensors used. Therefore, we discuss a method for calibration of the sensor with respect to the robot end-effector, which is straightforward to apply on an arbitrary industrial manipulator. We also consider two different workspace state-estimation algorithms requiring a minimum of robot modeling; the first is based on the extended Kalman filter and the second is based on the Rao-Blackwellized particle filter. The calibration procedure and the state-estimation... (More)
We consider the problem of tool position and orientation state estimation for robot manipulators in workspace by sensor fusion of the internal robot joint measurements with inertial measurement unit data. A prerequisite for this to be successful is accurate calibration of the sensors used. Therefore, we discuss a method for calibration of the sensor with respect to the robot end-effector, which is straightforward to apply on an arbitrary industrial manipulator. We also consider two different workspace state-estimation algorithms requiring a minimum of robot modeling; the first is based on the extended Kalman filter and the second is based on the Rao-Blackwellized particle filter. The calibration procedure and the state-estimation algorithms were evaluated and compared in extensive experiments. Both state-estimation algorithms exhibited an accuracy improvement compared to estimates provided by the forward kinematics of the robot. Moreover, both algorithms were shown to satisfy the constraints of real-time execution at 4-ms sampling period. To further evaluate and compare the robustness of the methods, the algorithms were investigated with respect to the sensitivity of the filter parameters and the noise modeling.
(Less)
- author
- Olofsson, Björn
LU
; Bergstedt, Jacob
LU
; Kortier, Henk G.
; Bernhardsson, Bo
LU
; Robertsson, Anders
LU
and Johansson, Rolf
LU
- organization
- publishing date
- 2016-10-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Particle filters, robots, state estimation
- in
- IEEE/ASME Transactions on Mechatronics
- volume
- 21
- issue
- 5
- article number
- 7347433
- pages
- 13 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- wos:000382472600004
- scopus:84983512706
- ISSN
- 1083-4435
- DOI
- 10.1109/TMECH.2015.2506041
- project
- RobotLab LTH
- language
- English
- LU publication?
- yes
- id
- df825079-f297-4a73-9176-924e61f53bad
- date added to LUP
- 2016-10-17 11:18:03
- date last changed
- 2024-04-05 08:11:54
@article{df825079-f297-4a73-9176-924e61f53bad,
abstract = {{<p>We consider the problem of tool position and orientation state estimation for robot manipulators in workspace by sensor fusion of the internal robot joint measurements with inertial measurement unit data. A prerequisite for this to be successful is accurate calibration of the sensors used. Therefore, we discuss a method for calibration of the sensor with respect to the robot end-effector, which is straightforward to apply on an arbitrary industrial manipulator. We also consider two different workspace state-estimation algorithms requiring a minimum of robot modeling; the first is based on the extended Kalman filter and the second is based on the Rao-Blackwellized particle filter. The calibration procedure and the state-estimation algorithms were evaluated and compared in extensive experiments. Both state-estimation algorithms exhibited an accuracy improvement compared to estimates provided by the forward kinematics of the robot. Moreover, both algorithms were shown to satisfy the constraints of real-time execution at 4-ms sampling period. To further evaluate and compare the robustness of the methods, the algorithms were investigated with respect to the sensitivity of the filter parameters and the noise modeling.</p>}},
author = {{Olofsson, Björn and Bergstedt, Jacob and Kortier, Henk G. and Bernhardsson, Bo and Robertsson, Anders and Johansson, Rolf}},
issn = {{1083-4435}},
keywords = {{Particle filters; robots; state estimation}},
language = {{eng}},
month = {{10}},
number = {{5}},
pages = {{2236--2248}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{IEEE/ASME Transactions on Mechatronics}},
title = {{Sensor Fusion for Robotic Workspace State Estimation}},
url = {{http://dx.doi.org/10.1109/TMECH.2015.2506041}},
doi = {{10.1109/TMECH.2015.2506041}},
volume = {{21}},
year = {{2016}},
}