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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.

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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Particle filters, robots, state estimation
in
IEEE/ASME Transactions on Mechatronics
volume
21
issue
5
pages
13 pages
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • scopus:84983512706
  • wos:000382472600004
ISSN
1083-4435
DOI
10.1109/TMECH.2015.2506041
language
English
LU publication?
yes
id
df825079-f297-4a73-9176-924e61f53bad
date added to LUP
2016-10-17 11:18:03
date last changed
2017-01-01 08:37:02
@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>},
  articleno    = {7347433},
  author       = {Olofsson, Björn and Bergstedt, Jacob and Kortier, Henk G. and Bernhardsson, Bo and Robertsson, Anders and Johansson, Rolf},
  issn         = {1083-4435},
  keyword      = {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},
  volume       = {21},
  year         = {2016},
}