Calibration of 6-DOF industrial robots based on line structured light

Abstract To improve the accuracy of an industrial 6-DOF robot, this paper proposes a new calibration method based on a line structured light measurement system. The line structured light measurement system mounted on the end-effector is used as a tool to acquire the three-dimensional coordinate information of the centre of a standard sphere. Hand-eye calibration is used to establish the transformation between the robot and the measurement system. A combined kinematic model that includes both the kinematic parameters of the robot and the initial hand-eye relationship is established by applying the modified Denavit and Hartenberg (MDH) model. An error model is derived based on the principle that the coordinate of the sphere centre is invariant in the base frame of the robot. Consequently, the errors in the kinematic parameters of the robot and the hand-eye relationship can be identified by substituting the measurement results and the original parameters of the robot into the error model. The results of the simulations and experiments show that the accuracy of the robot is significantly improved by implementing the proposed calibration method. The proposed method is effective, simple, time-saving and low-cost and is therefore a suitable candidate for on-site calibration of industrial robots.

[1]  Hanqi Zhuang,et al.  A complete and parametrically continuous kinematic model for robot manipulators , 1990, Proceedings., IEEE International Conference on Robotics and Automation.

[2]  Arthur C. Sanderson,et al.  A prototype arm signature identification system , 1987, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

[3]  Yin Guo,et al.  Development and calibration of an integrated 3D scanning system for high-accuracy large-scale metrology , 2014 .

[4]  John P. Fulton,et al.  Simultaneous Moisture Content and Mass Flow Measurements in Wood Chip Flows Using Coupled Dielectric and Impact Sensors , 2016, Sensors.

[5]  Adam Wozniak,et al.  Novel CMM-based implementation of the multi-step method for the separation of machine and probe errors , 2011 .

[6]  Marek Płaczek,et al.  Testing of an industrial robot’s accuracy and repeatability in off and online environment , 2018, Eksploatacja i Niezawodnosc - Maintenance and Reliability.

[7]  Ilian A. Bonev,et al.  Online pose correction of an industrial robot using an optical coordinate measure machine system , 2018, International Journal of Advanced Robotic Systems.

[8]  Wenfu Xu,et al.  An Efficient Pose Measurement Method of a Space Non-Cooperative Target Based on Stereo Vision , 2017, IEEE Access.

[9]  Ken Chen,et al.  A Minimal POE-Based Model for Robotic Kinematic Calibration With Only Position Measurements , 2015, IEEE Transactions on Automation Science and Engineering.

[10]  Alex de Sherbinin,et al.  Landsat-Derived Estimates of Mangrove Extents in the Sierra Leone Coastal Landscape Complex during 1990–2016 , 2017, Sensors.

[11]  Samad Hayati,et al.  Improving the absolute positioning accuracy of robot manipulators , 1985, J. Field Robotics.

[12]  Sukhan Lee,et al.  A robot-camera hand/eye self-calibration system using a planar target , 2013, IEEE ISR 2013.

[13]  J. Denavit,et al.  A kinematic notation for lower pair mechanisms based on matrices , 1955 .

[14]  Ilian A. Bonev,et al.  Absolute calibration of an ABB IRB 1600 robot using a laser tracker , 2013 .

[15]  A. Olabi,et al.  Improving the accuracy of industrial robots by offline compensation of joints errors , 2012, 2012 IEEE International Conference on Industrial Technology.

[16]  Qiang Huang,et al.  A New Kind of Accurate Calibration Method for Robotic Kinematic Parameters Based on the Extended Kalman and Particle Filter Algorithm , 2018, IEEE Transactions on Industrial Electronics.

[17]  Jie Zhang,et al.  Error Modeling and Experimental Study of a Flexible Joint 6-UPUR Parallel Six-Axis Force Sensor , 2017, Sensors.

[18]  Xiaorong Gao,et al.  Design of the Fall-Block Sensing of the Railway Line Pantograph Based on 3D Machine Vision Sensors , 2018, Sensors.

[19]  Ping Ren,et al.  Calibration of a flexible measurement system based on industrial articulated robot and structured light sensor , 2017 .

[20]  Chen-Gang,et al.  Review on kinematics calibration technology of serial robots , 2014 .

[21]  Adrian Burlacu,et al.  Orthogonal dual tensor method for solving the AX = XB sensor calibration problem , 2016 .

[22]  Yong-Lin Kuo,et al.  Pose Determination of a Robot Manipulator Based on Monocular Vision , 2016, IEEE Access.

[23]  Ilian A. Bonev,et al.  Comparison of two calibration methods for a small industrial robot based on an optical CMM and a laser tracker , 2013, Robotica.

[24]  Ilian A. Bonev,et al.  Kinematic calibration of a 3-DOF planar parallel robot , 2012, Ind. Robot.

[25]  I. Bonev,et al.  Kinematic calibration of a five-bar planar parallel robot using all working modes , 2013 .

[26]  Wang Xu,et al.  Complete calibration of industrial robot with limited parameters and neural network , 2016, 2016 IEEE International Symposium on Robotics and Intelligent Sensors (IRIS).

[27]  Olav Egeland,et al.  Mechanical Design Optimization of a 6DOF Serial Manipulator Using Genetic Algorithm , 2018, IEEE Access.

[28]  Hongliang Ren,et al.  Simultaneous Robot-World, Sensor-Tip, and Kinematics Calibration of an Underactuated Robotic Hand With Soft Fingers , 2018, IEEE Access.

[29]  Chenglin Dong,et al.  Kinematic Calibration Based on the Multicollinearity Diagnosis of a 6-DOF Polishing Hybrid Robot Using a Laser Tracker , 2018, Mathematical Problems in Engineering.