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

Matteo Rubagotti, Tasbolat Taunyazov, Bukeikhan Omarali, Almas Shintemirov

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


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 languageEnglish
Article number8718327
Pages (from-to)2746-2753
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number3
Publication statusPublished - Jul 2019


  • 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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