A virtual force sensor for interaction tasks with conventional industrial robots

Abstract The attempt to use industrial robots for technological and interaction tasks, i.e., robotic machining and robotic assembling, implies on the one hand the knowledge of the interaction force, on the other hand the reduction of physical sensors. The aim of this work is the development of a virtual force sensor to estimate the interaction force between a conventional industrial robot and the environment. The goal is achieved by exploiting a task oriented dynamics model calibration combined with of a thermal friction model of the robot. The dynamics model is calibrated by means of exciting trajectories made by suitable paths selected by a genetic-based two-stage optimization. The virtual sensor is proven by means of a polishing application. The proposed approach is successfully compared with state-of-the-art approaches. Finally, the use of the virtual force sensor in a closed-loop architecture highlights the effectiveness of the method in real applications.

[1]  Manuel Beschi,et al.  A general analytical procedure for robot dynamic model reduction , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[2]  Giuseppe Carlo Calafiore,et al.  Robot Dynamic Calibration: Optimal Excitation Trajectories and Experimental Parameter Estimation , 2001 .

[3]  Kyung-Jo Park,et al.  Fourier-based optimal excitation trajectories for the dynamic identification of robots , 2006, Robotica.

[4]  Alessandro De Luca,et al.  Collision Detection and Safe Reaction with the DLR-III Lightweight Manipulator Arm , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  Antonio Visioli,et al.  Friction modeling with temperature effects for industrial robot manipulators , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[6]  V. van Geffen,et al.  A study of friction models and friction compensation , 2009 .

[7]  Jan Swevers,et al.  Optimal robot excitation and identification , 1997, IEEE Trans. Robotics Autom..

[8]  Jun Wu,et al.  Review: An overview of dynamic parameter identification of robots , 2010 .

[9]  Francesco Braghin,et al.  Optimal Impedance Force-Tracking Control Design With Impact Formulation for Interaction Tasks , 2016, IEEE Robotics and Automation Letters.

[10]  Pasquale Chiacchio,et al.  Identification of dynamic parameters and feedforward control for a conventional industrial manipulator , 1994 .

[11]  Koichi Osuka,et al.  Base parameters of manipulator dynamic models , 1990, IEEE Trans. Robotics Autom..

[12]  Matteo Parigi Polverini,et al.  Sensorless and constraint based peg-in-hole task execution with a dual-arm robot , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[13]  Anders Robertsson,et al.  Robotic force estimation using motor torques and modeling of low velocity friction disturbances , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[14]  T. Brogårdh,et al.  Robot Control Overview: An Industrial Perspective , 2009 .

[15]  Mahdi Tavakoli,et al.  Disturbance Observer Based Control of Nonlinear Haptic Teleoperation Systems , 2011 .

[16]  Maxime Gautier,et al.  Identification of robot dynamic parameters using Jacobi differentiator , 2015, 2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM).

[17]  Peter J. Gawthrop,et al.  A nonlinear disturbance observer for robotic manipulators , 2000, IEEE Trans. Ind. Electron..

[18]  Maxime Gautier Dynamic identification of robots with power model , 1997, Proceedings of International Conference on Robotics and Automation.

[19]  Wisama Khalil,et al.  On the identification of the inertial parameters of robots , 1988, Proceedings of the 27th IEEE Conference on Decision and Control.

[20]  Anders Robertsson,et al.  On force control for assembly and deburring of castings , 2013, Prod. Eng..

[21]  M. Gautier,et al.  Exciting Trajectories for the Identification of Base Inertial Parameters of Robots , 1992 .

[22]  Maxime Gautier,et al.  A Generic Instrumental Variable Approach for Industrial Robot Identification , 2014, IEEE Transactions on Control Systems Technology.

[23]  H. Dankowicz On the modeling of dynamic friction phenomena , 1999 .

[24]  Guido Herrmann,et al.  A novel robust adaptive control algorithm with finite-time online parameter estimation of a humanoid robot arm , 2014, Robotics Auton. Syst..

[25]  Friedrich Lange,et al.  Revised force control using a compliant sensor with a position controlled robot , 2012, 2012 IEEE International Conference on Robotics and Automation.

[26]  Lorenzo Molinari Tosatti,et al.  Optimal robot dynamics local identification using genetic-based path planning in workspace subregions , 2013, 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics.

[27]  Alessandro De Luca,et al.  Estimation of contact forces using a virtual force sensor , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[28]  K.J. Astrom,et al.  Revisiting the LuGre friction model , 2008, IEEE Control Systems.

[29]  Reza Haghighi,et al.  Lyapunov-Based Nonlinear Disturbance Observer for Serial n-Link Robot Manipulators , 2009, J. Intell. Robotic Syst..

[30]  Anders Robertsson,et al.  Modeling and identification of position and temperature dependent friction phenomena without temperature sensing , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[31]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[32]  Maxime Gautier,et al.  A New Closed-Loop Output Error Method for Parameter Identification of Robot Dynamics , 2010, IEEE Transactions on Control Systems Technology.

[33]  F. Alonge,et al.  Interaction control of robotic manipulators without force measurement , 2010, 2010 IEEE International Symposium on Industrial Electronics.

[34]  Carlos Canudas de Wit,et al.  A new model for control of systems with friction , 1995, IEEE Trans. Autom. Control..

[35]  Lorenzo Molinari Tosatti,et al.  Robot-dynamic calibration improvement by local identification , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).