TY - GEN
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:
This work was funded under the Nazarbayev University faculty development grant project ?Development of an Intelligent Assistive Robot Manipulation System for Improving the Quality of Life of Disabled People iKazakhstan? (Project PI: A. Shintemirov)
Publisher Copyright:
© 2019, Robotics: Science and Systems. All rights reserved.
PY - 2019
Y1 - 2019
N2 - This paper1 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 paper1 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).
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U2 - 10.15607/RSS.2019.XV.081
DO - 10.15607/RSS.2019.XV.081
M3 - Conference contribution
AN - SCOPUS:85127921723
SN - 9780992374754
T3 - Robotics: Science and Systems
BT - Robotics
A2 - Bicchi, Antonio
A2 - Kress-Gazit, Hadas
A2 - Hutchinson, Seth
PB - MIT Press Journals
T2 - 15th Robotics: Science and Systems, RSS 2019
Y2 - 22 June 2019 through 26 June 2019
ER -