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Joint Stiction Avoidance with Null-Space Motion in Real-Time Model Predictive Control for Redundant Collaborative Robots

Salt Ducaju, Julian M. LU orcid ; Olofsson, Bjorn LU ; Robertsson, Anders LU and Johansson, Rolf LU orcid (2021) 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021 In IEEE International Conference on Robot and Human Interactive Communication, RO-MAN p.307-314
Abstract

Model Predictive Control (MPC) is an efficient point-to-point trajectory-generation method for robots that can be used in situations that occur under time constraints. The motion plan can be recalculated online to increase the accuracy of the trajectory when getting close to the goal position. We have implemented this strategy in a Franka Emika Panda robot, a redundant collaborative robot, by extending previous research that was performed on a 6-DOF robot. We have also used null-space motion to ensure a continuous movement of all joints during the entire trajectory execution as an approach to avoid joint stiction and allow accurate kinesthetic teaching. As is conventional for collaborative and industrial robots, the Panda robot is... (More)

Model Predictive Control (MPC) is an efficient point-to-point trajectory-generation method for robots that can be used in situations that occur under time constraints. The motion plan can be recalculated online to increase the accuracy of the trajectory when getting close to the goal position. We have implemented this strategy in a Franka Emika Panda robot, a redundant collaborative robot, by extending previous research that was performed on a 6-DOF robot. We have also used null-space motion to ensure a continuous movement of all joints during the entire trajectory execution as an approach to avoid joint stiction and allow accurate kinesthetic teaching. As is conventional for collaborative and industrial robots, the Panda robot is equipped with an internal controller, which allows to send position and velocity references directly to the robot. Therefore, null-space motion can be added directly to the MPC-generated velocity references. The observed trajectory deviation caused by discretization approximations of the Jacobian matrix when implementing null-space motion has been corrected experimentally using sensor feedback for the real-time velocity-reference recalculation and by performing a fast sampling of the null-space vector. Null-space motion has been experimentally seen to contribute to reducing the friction torque dispersion present in static joints.

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Please use this url to cite or link to this publication:
author
; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021
series title
IEEE International Conference on Robot and Human Interactive Communication, RO-MAN
pages
8 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021
conference location
Virtual, Vancouver, Canada
conference dates
2021-08-08 - 2021-08-12
external identifiers
  • scopus:85115100144
ISSN
1944-9445
1944-9437
ISBN
9781665404921
DOI
10.1109/RO-MAN50785.2021.9515514
project
RobotLab LTH
Human-Robot Collaboration for Kinesthetic Teaching
language
English
LU publication?
yes
id
fb129a30-a41d-401a-8b5e-0c83a97a53fb
date added to LUP
2021-10-04 11:13:11
date last changed
2024-06-29 18:23:06
@inproceedings{fb129a30-a41d-401a-8b5e-0c83a97a53fb,
  abstract     = {{<p>Model Predictive Control (MPC) is an efficient point-to-point trajectory-generation method for robots that can be used in situations that occur under time constraints. The motion plan can be recalculated online to increase the accuracy of the trajectory when getting close to the goal position. We have implemented this strategy in a Franka Emika Panda robot, a redundant collaborative robot, by extending previous research that was performed on a 6-DOF robot. We have also used null-space motion to ensure a continuous movement of all joints during the entire trajectory execution as an approach to avoid joint stiction and allow accurate kinesthetic teaching. As is conventional for collaborative and industrial robots, the Panda robot is equipped with an internal controller, which allows to send position and velocity references directly to the robot. Therefore, null-space motion can be added directly to the MPC-generated velocity references. The observed trajectory deviation caused by discretization approximations of the Jacobian matrix when implementing null-space motion has been corrected experimentally using sensor feedback for the real-time velocity-reference recalculation and by performing a fast sampling of the null-space vector. Null-space motion has been experimentally seen to contribute to reducing the friction torque dispersion present in static joints.</p>}},
  author       = {{Salt Ducaju, Julian M. and Olofsson, Bjorn and Robertsson, Anders and Johansson, Rolf}},
  booktitle    = {{2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021}},
  isbn         = {{9781665404921}},
  issn         = {{1944-9445}},
  language     = {{eng}},
  month        = {{08}},
  pages        = {{307--314}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{IEEE International Conference on Robot and Human Interactive Communication, RO-MAN}},
  title        = {{Joint Stiction Avoidance with Null-Space Motion in Real-Time Model Predictive Control for Redundant Collaborative Robots}},
  url          = {{https://lup.lub.lu.se/search/files/160718327/RO_MAN21_FINAL_5_.pdf}},
  doi          = {{10.1109/RO-MAN50785.2021.9515514}},
  year         = {{2021}},
}