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

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).

[1]  J Vertut,et al.  Teleoperation and robotics :: applications and technology , 1987 .

[2]  Tobias Weber,et al.  Implementation of Nonlinear Model Predictive Path-Following Control for an Industrial Robot , 2015, IEEE Transactions on Control Systems Technology.

[3]  Mohammad Farrokhi,et al.  Adaptive neuro‐predictive control for redundant robot manipulators in presence of static and dynamic obstacles: A Lyapunov‐based approach , 2014 .

[4]  Olivier Stasse,et al.  Whole-body model-predictive control applied to the HRP-2 humanoid , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[5]  Ronald Azuma,et al.  A Survey of Augmented Reality , 1997, Presence: Teleoperators & Virtual Environments.

[6]  Thomas Timm Andersen.  Optimizing the Universal Robots ROS driver. , 2015 .

[7]  Frans C. T. van der Helm,et al.  A Task-Specific Analysis of the Benefit of Haptic Shared Control During Telemanipulation , 2013, IEEE Transactions on Haptics.

[8]  Antonio Ferramosca,et al.  Nonlinear MPC for Tracking Piece-Wise Constant Reference Signals , 2018, IEEE Transactions on Automatic Control.

[9]  David Q. Mayne,et al.  Model predictive control: Recent developments and future promise , 2014, Autom..

[10]  Christian Kirches,et al.  qpOASES: a parametric active-set algorithm for quadratic programming , 2014, Mathematical Programming Computation.

[11]  Leonardo Meli,et al.  Multicontact Bilateral Telemanipulation With Kinematic Asymmetries , 2017, IEEE/ASME Transactions on Mechatronics.

[12]  Kyrre Glette,et al.  An Ultrasound Robotic System Using the Commercial Robot UR5 , 2016, Front. Robot. AI.

[13]  Matteo Rubagotti,et al.  Closed-Loop Control of Variable Stiffness Actuated Robots via Nonlinear Model Predictive Control , 2015, IEEE Access.

[14]  John D. Childs,et al.  A review of space robotics technologies for on-orbit servicing , 2015 .

[15]  Bernhard Schölkopf,et al.  Probabilistic movement modeling for intention inference in human–robot interaction , 2013, Int. J. Robotics Res..

[16]  Martin Buss,et al.  A Survey of Environment-, Operator-, and Task-adapted Controllers for Teleoperation Systems , 2010 .

[17]  Yuval Tassa,et al.  Real-time behaviour synthesis for dynamic hand-manipulation , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[18]  Angelika Zube.  Cartesian nonlinear model predictive control of redundant manipulators considering obstacles , 2015, 2015 IEEE International Conference on Industrial Technology (ICIT).

[19]  Andrea Maria Zanchettin,et al.  Constrained model predictive control for mobile robotic manipulators , 2017, Robotica.

[20]  Moritz Diehl,et al.  ACADO toolkit—An open‐source framework for automatic control and dynamic optimization , 2011 .

[21]  Pedro Sanz.  Robotics: Modeling, Planning, and Control (Siciliano, B. et al; 2009) [On the Shelf] , 2009, IEEE Robotics & Automation Magazine.

[22]  Pantelis Sopasakis,et al.  A simple and efficient algorithm for nonlinear model predictive control , 2017, 2017 IEEE 56th Annual Conference on Decision and Control (CDC).

[23]  Advait Jain,et al.  Reaching in clutter with whole-arm tactile sensing , 2013, Int. J. Robotics Res..

[24]  Antonella Ferrara,et al.  MPC for Robot Manipulators With Integral Sliding Modes Generation , 2017, IEEE/ASME Transactions on Mechatronics.

[25]  Jianjun Luo,et al.  A non-linear model predictive controller with obstacle avoidance for a space robot , 2016 .

[26]  Yukang Liu,et al.  Toward Welding Robot With Human Knowledge: A Remotely-Controlled Approach , 2015, IEEE Transactions on Automation Science and Engineering.

[27]  Russell H. Taylor,et al.  Medical robotics in computer-integrated surgery , 2003, IEEE Trans. Robotics Autom..

[28]  Russell H. Taylor,et al.  Medical robotics in computer-integrated surgery , 2003, IEEE Trans. Robotics Autom..

[29]  Magnus Egerstedt,et al.  Less Is More: Mixed-Initiative Model-Predictive Control With Human Inputs , 2013, IEEE Transactions on Robotics.

[30]  Charles C. Kemp,et al.  Model predictive control for fast reaching in clutter , 2016, Auton. Robots.

[31]  Stephen P. Boyd,et al.  CVXGEN: a code generator for embedded convex optimization , 2011, Optimization and Engineering.

[32]  Mark W. Spong,et al.  Bilateral teleoperation: An historical survey , 2006, Autom..

[33]  Lihui Wang,et al.  Human motion prediction for human-robot collaboration , 2017 .

[34]  Piyush Kumar,et al.  Minimum-Volume Enclosing Ellipsoids and Core Sets , 2005 .

[35]  Almas Shintemirov,et al.  A novel low-cost 4-DOF wireless human arm motion tracker , 2016, 2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob).