Robotics: Science and Systems XV

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

Matteo Rubagotti, Tasbolat Taunyazov, Bukeikhan Omarali, Almas Shintemirov


This paper proposes a model predictive control (MPC) approach for semi-autonomous teleoperation of robot manipulators: the focus is on automatically avoiding singular configurations of the robot and obstacles in the robot workspace with the whole robot frame, 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 (including, in addition to those above mentioned, limits on joint accelerations, velocities and positions). An experimental case study is described regarding the design and testing of the proposed framework on a UR5 manipulator: the discussion of the experimental results confirms the suitability of the proposed method for semi-autonomous robot teleoperation, both in terms of performance (tracking capability and constraint satisfaction) and computational complexity (the MPC control law is calculated well within the sampling interval).



    AUTHOR    = {Matteo Rubagotti AND Tasbolat Taunyazov AND Bukeikhan Omarali AND Almas Shintemirov}, 
    TITLE     = {Semi-Autonomous Robot Teleoperation with Obstacle Avoidance Via Model Predictive Control}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2019}, 
    ADDRESS   = {FreiburgimBreisgau, Germany}, 
    MONTH     = {June}, 
    DOI       = {10.15607/RSS.2019.XV.081}