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Robot Tool Calibration of an Active Pen with Python using an Enabled Surface from Anoto Technology

Sjöberg, Jonny LU (2018) FRT820 20181
Department of Automatic Control
Abstract
This master thesis has shown multiple uses of the Anoto dot pattern and Anoto pen for different applications. One application is the calibration of the robot tool and work object. Another is the use of an Anoto pen and paper as replacement of the ABB Flexpendant to create a lightweight and intuitive control device to control an industrial robot, experimentally demonstrated for the IRB140 robot arm. The Python programming language and interpreter were used together with ABB’s
RAPID programming language to create a framework to handle the communication between the user and the robot.
The inverse and forward kinematics were derived for the robot and used for simulations and for determining if a trajectory could be traversed. This helped in... (More)
This master thesis has shown multiple uses of the Anoto dot pattern and Anoto pen for different applications. One application is the calibration of the robot tool and work object. Another is the use of an Anoto pen and paper as replacement of the ABB Flexpendant to create a lightweight and intuitive control device to control an industrial robot, experimentally demonstrated for the IRB140 robot arm. The Python programming language and interpreter were used together with ABB’s
RAPID programming language to create a framework to handle the communication between the user and the robot.
The inverse and forward kinematics were derived for the robot and used for simulations and for determining if a trajectory could be traversed. This helped in the
theoretical verification in the development of the calibration algorithm. The calibration details from the results section show that a naive approach to calibrating the tool tip, i.e., using all of the measurement values results in a deviation of 1mm or more with slow convergence compared to the manual calibration of 0.5mm deviation. By using a greedy optimization strategy of successively adding measurements that improve the calibration, and removing measurements that worsen it, we get a calibration comparable to a manual calibration with fast convergence.
The work object calibration, i.e., the orientation estimation of the Anoto pattern surface shows promising results with few measurements and faster convergence,
but can also be improved with the same optimization strategy. By performing manual measurements, and using measurements of the error in the robot flange position, a realistic lower bound on the precision of the calibration algorithm was decided to be no lower than 0.3 - 0.4mm, and depended on the performance of the pen. The flexpendant control board replacement was developed through a user study with volunteers from Anoto. (Less)
Please use this url to cite or link to this publication:
author
Sjöberg, Jonny LU
supervisor
organization
course
FRT820 20181
year
type
H3 - Professional qualifications (4 Years - )
subject
report number
TFRT-6051
ISSN
0280-5316
language
English
id
8939295
date added to LUP
2018-05-04 09:32:03
date last changed
2018-05-09 14:48:12
@misc{8939295,
  abstract     = {This master thesis has shown multiple uses of the Anoto dot pattern and Anoto pen for different applications. One application is the calibration of the robot tool and work object. Another is the use of an Anoto pen and paper as replacement of the ABB Flexpendant to create a lightweight and intuitive control device to control an industrial robot, experimentally demonstrated for the IRB140 robot arm. The Python programming language and interpreter were used together with ABB’s
RAPID programming language to create a framework to handle the communication between the user and the robot.
The inverse and forward kinematics were derived for the robot and used for simulations and for determining if a trajectory could be traversed. This helped in the
theoretical verification in the development of the calibration algorithm. The calibration details from the results section show that a naive approach to calibrating the tool tip, i.e., using all of the measurement values results in a deviation of 1mm or more with slow convergence compared to the manual calibration of 0.5mm deviation. By using a greedy optimization strategy of successively adding measurements that improve the calibration, and removing measurements that worsen it, we get a calibration comparable to a manual calibration with fast convergence.
The work object calibration, i.e., the orientation estimation of the Anoto pattern surface shows promising results with few measurements and faster convergence,
but can also be improved with the same optimization strategy. By performing manual measurements, and using measurements of the error in the robot flange position, a realistic lower bound on the precision of the calibration algorithm was decided to be no lower than 0.3 - 0.4mm, and depended on the performance of the pen. The flexpendant control board replacement was developed through a user study with volunteers from Anoto.},
  author       = {Sjöberg, Jonny},
  issn         = {0280-5316},
  language     = {eng},
  note         = {Student Paper},
  title        = {Robot Tool Calibration of an Active Pen with Python using an Enabled Surface from Anoto Technology},
  year         = {2018},
}