Semi-Autonomous Robot Teleoperation with Obstacle Avoidance via Model Predictive Control

Matteo Rubagotti; Tasbolat Taunyazov; Bukeikhan Omarali; Almas Shintemirov

doi:10.1109/LRA.2019.2917707

Semi-Autonomous Robot Teleoperation with Obstacle Avoidance via Model Predictive Control

Matteo Rubagotti, Tasbolat Taunyazov, Bukeikhan Omarali, Almas Shintemirov

National University of Singapore

Research output: Contribution to journal › Article › peer-review

63 Citations (Scopus)

Abstract

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).

Original language	English
Article number	8718327
Pages (from-to)	2746-2753
Number of pages	8
Journal	IEEE Robotics and Automation Letters
Volume	4
Issue number	3
DOIs	https://doi.org/10.1109/LRA.2019.2917707
Publication status	Published - Jul 2019

Funding

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: [email protected]; [email protected]).

Keywords

Optimization and optimal control
industrial robots
motion and path planning

ASJC Scopus subject areas

Control and Systems Engineering
Biomedical Engineering
Human-Computer Interaction
Mechanical Engineering
Computer Vision and Pattern Recognition
Computer Science Applications
Control and Optimization
Artificial Intelligence

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10.1109/LRA.2019.2917707

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title = "Semi-Autonomous Robot Teleoperation with Obstacle Avoidance via Model Predictive Control",

abstract = "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).",

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note = "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{\textquoteright} 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: [email protected]; [email protected]). Publisher Copyright: {\textcopyright} 2016 IEEE.",

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Semi-Autonomous Robot Teleoperation with Obstacle Avoidance via Model Predictive Control

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