Real-time predictive control of an ur5 robotic arm through human upper limb motion tracking

Bukeikhan Omarali, Tasbolat Taunyazov, Askhat Bukeyev, Almas Shintemirov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper reports the authors' initial results on developing a real-time teleoperation system for an Universal Robots robotic arm through human motion capture with a visualization utility built on the Blender Game Engine open-source platform. A linear explicit model predictive robot controller (EMPC) is implemented for online generation of optimal robot trajectories matching operator's wrist position and orientation, whilst adhering to the robot's constraints. The EMPC proved to be superior to open-loop and naive PID controllers in terms of accuracy and safety.

Original languageEnglish
Title of host publicationHRI 2017 - Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction
PublisherIEEE Computer Society
Pages237-238
Number of pages2
ISBN (Electronic)9781450348850
DOIs
Publication statusPublished - Mar 6 2017
Event12th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2017 - Vienna, Austria
Duration: Mar 6 2017Mar 9 2017

Conference

Conference12th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2017
CountryAustria
CityVienna
Period3/6/173/9/17

Keywords

  • model predictive control
  • motion tracking
  • real-time robot control
  • universal robots UR5

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Electrical and Electronic Engineering

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