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 proceedingConference contribution


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 languageEnglish
Title of host publicationRobotics
Subtitle of host publicationScience and Systems XV
EditorsAntonio Bicchi, Hadas Kress-Gazit, Seth Hutchinson
PublisherMIT Press Journals
ISBN (Print)9780992374754
Publication statusPublished - 2019
Event15th Robotics: Science and Systems, RSS 2019 - Freiburg im Breisgau, Germany
Duration: Jun 22 2019Jun 26 2019

Publication series

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


Conference15th Robotics: Science and Systems, RSS 2019
CityFreiburg im Breisgau

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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