TY - JOUR
T1 - Semi-Autonomous Robot Teleoperation with Obstacle Avoidance via Model Predictive Control
AU - Rubagotti, Matteo
AU - Taunyazov, Tasbolat
AU - Omarali, Bukeikhan
AU - Shintemirov, Almas
N1 - Funding Information:
Manuscript received January 4, 2019; accepted April 30, 2019. Date of publication May 20, 2019; date of current version May 30, 2019. This letter was recommended for publication by Associate Editor J. Xiao and Editor D. Song upon evaluation of the reviewers’ comments. This work was supported by the Nazarbayev University Faculty Development Grant Project “Development of an Intelligent Assistive Robot Manipulation System for Improving the Quality of Life of Disabled People in Kazakhstan.” T. Taunyazov and B. Omarali contributed equally. (Corresponding author: Almas Shintemirov.) M. Rubagotti and A. Shintemirov are with the Department of Robotics and Mechatronics, Nazarbayev University, Astana 010000, Kazakhstan (e-mail: rubagotti@ieee.org; ashintemirov@nu.edu.kz).
Publisher Copyright:
© 2016 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - This letter proposes a model predictive control approach for semi-autonomous teleoperation of robot manipulators: the focus is on avoiding obstacles with the whole robot frame, while exploiting predictions of the operator's motion. The hand pose of the human operator provides the reference for the end effector, and the robot motion is continuously replanned in real time, satisfying several constraints. An experimental case study is described regarding the design and testing of the described framework on a UR5 manipulator: the experimental results confirm the suitability of the proposed method for semi-autonomous teleoperation, both in terms of performance (tracking capability and constraint satisfaction) and computational complexity (the control law is calculated well within the sampling interval).
AB - This letter proposes a model predictive control approach for semi-autonomous teleoperation of robot manipulators: the focus is on avoiding obstacles with the whole robot frame, while exploiting predictions of the operator's motion. The hand pose of the human operator provides the reference for the end effector, and the robot motion is continuously replanned in real time, satisfying several constraints. An experimental case study is described regarding the design and testing of the described framework on a UR5 manipulator: the experimental results confirm the suitability of the proposed method for semi-autonomous teleoperation, both in terms of performance (tracking capability and constraint satisfaction) and computational complexity (the control law is calculated well within the sampling interval).
KW - Optimization and optimal control
KW - industrial robots
KW - motion and path planning
UR - http://www.scopus.com/inward/record.url?scp=85066948615&partnerID=8YFLogxK
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U2 - 10.1109/LRA.2019.2917707
DO - 10.1109/LRA.2019.2917707
M3 - Article
AN - SCOPUS:85066948615
VL - 4
SP - 2746
EP - 2753
JO - IEEE Robotics and Automation Letters
JF - IEEE Robotics and Automation Letters
SN - 2377-3766
IS - 3
M1 - 8718327
ER -