A Novel Indirect Calibration Approach for Robot Positioning Error Compensation Based on Neural Network and Hand-Eye Vision

It is well known that most of the industrial robots have excellent repeatability in positioning. However, the absolute position errors of industrial robots are relatively poor, and in some cases the error may reach even several millimeters, which make it difficult to apply the robot system to vehicle assembly lines that need small position errors. In this paper, we have studied a method to reduce the absolute position error of robots using machine vision and neural network. The position/orientation of robot tool-end is compensated using a vision-based approach combined with a neural network, where a novel indirect calibration approach is presented in order to gather information for training the neural network. In the simulation, the proposed compensation algorithm was found to reduce the positional error to 98%. On average, the absolute position error was 0.029 mm. The application of the proposed algorithm in the actual robot experiment reduced the error to 50.3%, averaging 1.79 mm.

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

[2]  Sukhan Lee,et al.  Stereo vision based automation for a bin-picking solution , 2012 .

[3]  Frank Chongwoo Park,et al.  Robot sensor calibration: solving AX=XB on the Euclidean group , 1994, IEEE Trans. Robotics Autom..

[4]  Andrew W. Fitzgibbon,et al.  A Buyer's Guide to Conic Fitting , 1995, BMVC.

[5]  Bahram Ravani,et al.  An overview of robot calibration , 1987, IEEE Journal on Robotics and Automation.

[6]  Shenghua Ye,et al.  A Vision-Based Self-Calibration Method for Robotic Visual Inspection Systems , 2013, Sensors.

[7]  Ying Bai,et al.  Experiment study of PUMA robot calibration using a laser tracking system , 2003, Proceedings of the 2003 IEEE International Workshop on Soft Computing in Industrial Applications, 2003. SMCia/03..

[8]  Olav Egeland,et al.  Automatic Touch-Up of Welding Paths Using 3D Vision , 2016 .

[9]  Jerzy E. Kurek,et al.  Application of Joint Error Mutual Compensation for Robot End-effector Pose Accuracy Improvement , 2003, J. Intell. Robotic Syst..

[10]  Ge Hong-mei.  Applications of Machine Vision in Packaging , 2010 .

[11]  Gerd Hirzinger,et al.  Optimal Hand-Eye Calibration , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[12]  Mao Jianfei,et al.  A direct linear solution with Jacobian optimization to AX=XB for hand-eye calibration , 2010 .

[13]  Ilian A. Bonev,et al.  Absolute robot calibration with a single telescoping ballbar , 2014 .

[14]  Daniel F. García,et al.  Robot Guidance Using Machine Vision Techniques in Industrial Environments: A Comparative Review , 2016, Sensors.

[15]  Jun Ni,et al.  Self-calibration method for robotic measurement system , 1999 .

[16]  Fumin Zhang,et al.  A Method for Improving the Pose Accuracy of a Robot Manipulator Based on Multi-Sensor Combined Measurement and Data Fusion , 2015, Sensors.

[17]  Ying Bai,et al.  Robot manipulator calibration using neural network and a camera-based measurement system , 2012 .

[18]  Y. Aiyama,et al.  Calibration of relative position between manipulator and work by Point-to-face touching method , 2009, 2009 IEEE International Symposium on Assembly and Manufacturing.

[19]  Wen-Chung Chang,et al.  Eye-in-hand vision-based robotic bin-picking with active laser projection , 2015, The International Journal of Advanced Manufacturing Technology.

[20]  Marvin K. Bugeja,et al.  A review on multi-robot systems categorised by application domain , 2017, 2017 25th Mediterranean Conference on Control and Automation (MED).

[21]  Christian Wöhler,et al.  3D Computer Vision - Efficient Methods and Applications , 2009, X.media.publishing.

[22]  Chao Zhang,et al.  Binary Output Layer of Feedforward Neural Networks for Solving Multi-Class Classification Problems , 2019, IEEE Access.

[23]  Éric Marchand,et al.  Pose Estimation for Augmented Reality: A Hands-On Survey , 2016, IEEE Transactions on Visualization and Computer Graphics.

[24]  Hanqi Zhuang,et al.  Autonomous robot calibration using vision technology , 2007 .

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

[26]  Ying Bai,et al.  Modeless robots calibration in 3D workspace with an on-line fuzzy interpolation technique , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[27]  In-Chul Ha.  Kinematic parameter calibration method for industrial robot manipulator using the relative position , 2008 .