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

Matteo Rubagotti; Tasbolat Taunyazov; Bukeikhan Omarali; Almas Shintemirov

doi:10.15607/RSS.2019.XV.081

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

Matteo Rubagotti
, Tasbolat Taunyazov
, Bukeikhan Omarali
, Almas Shintemirov

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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).

Original language	English
Title of host publication	Robotics
Subtitle of host publication	Science and Systems XV
Editors	Antonio Bicchi, Hadas Kress-Gazit, Seth Hutchinson
Publisher	MIT Press Journals
ISBN (Print)	9780992374754
DOIs	https://doi.org/10.15607/RSS.2019.XV.081
Publication status	Published - 2019
Event	15th Robotics: Science and Systems, RSS 2019 - Freiburg im Breisgau, Germany Duration: Jun 22 2019 → Jun 26 2019

Publication series

Name	Robotics: Science and Systems
ISSN (Electronic)	2330-765X

Conference

Conference	15th Robotics: Science and Systems, RSS 2019
Country/Territory	Germany
City	Freiburg im Breisgau
Period	6/22/19 → 6/26/19

Funding

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)

ASJC Scopus subject areas

Artificial Intelligence
Control and Systems Engineering
Electrical and Electronic Engineering

Access to Document

10.15607/RSS.2019.XV.081

Semi-Autonomous Robot Teleoperation with Obstacle Avoidance Via Model Predictive Control 866a90ccFinal published version, 1.61 MB

Cite this

Semi-Autonomous Robot Teleoperation with Obstacle Avoidance via Model Predictive Control. / Rubagotti, Matteo; Taunyazov, Tasbolat; Omarali, Bukeikhan et al.
Robotics: Science and Systems XV. ed. / Antonio Bicchi; Hadas Kress-Gazit; Seth Hutchinson. MIT Press Journals, 2019. (Robotics: Science and Systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Rubagotti, M, Taunyazov, T, Omarali, B & Shintemirov, A 2019, Semi-Autonomous Robot Teleoperation with Obstacle Avoidance via Model Predictive Control. in A Bicchi, H Kress-Gazit & S Hutchinson (eds), Robotics: Science and Systems XV. Robotics: Science and Systems, MIT Press Journals, 15th Robotics: Science and Systems, RSS 2019, Freiburg im Breisgau, Germany, 6/22/19. https://doi.org/10.15607/RSS.2019.XV.081

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abstract = "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{\textquoteright}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).",

author = "Matteo Rubagotti and Tasbolat Taunyazov and Bukeikhan Omarali and Almas Shintemirov",

note = "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: {\textcopyright} 2019, Robotics: Science and Systems. All rights reserved.; 15th Robotics: Science and Systems, RSS 2019 ; Conference date: 22-06-2019 Through 26-06-2019",

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doi = "10.15607/RSS.2019.XV.081",

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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

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

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