Abstract
Kinematics calibration for quadrupled robots is essential to ensuring motion accuracy and control stability. The angle of the leg joints of the quadruped robot is error-compensated to improve its position accuracy. This paper proposes a real-time high-precision kinematics calibration method for quadruped robots using machine vision and artificial neural networks to simplify the calibration process and improve calibration accuracy. The method includes two parts: identifying the markers fixed on the legs through target detection, calculating the center coordinates of the markers, and building an error model based on an artificial neural network to solve the angle error of each joint and compensate for it. A series of experiments have been carried out to verify the model’s accuracy. The experimental results show that, compared to traditional manual calibration, by adding an error correction model to the inverse kinematics neural network, the calibration efficiency can be significantly improved while the calibration accuracy is met.
References
1.
Moro
, F. L.
, Spröwitz
, A.
, Tuleu
, A.
, Vespignani
, M.
, Tsagarakis
, N. G.
, Ijspeert
, A. J.
, and Caldwell
, D. G.
, 2013
, “Horse-Like Walking, Trotting, and Galloping Derived From Kinematic Motion Primitives (KMPs) and Their Application to Walk/Trot Transitions in a Compliant Quadruped Robot
,” Biol. Cybern.
, 107
(3
), pp. 309
–320
. 2.
Hooks
, J.
, Ahn
, M. S.
, Yu
, J.
, Zhang
, X.
, Zhu
, T.
, Chae
, H.
, and Hong
, D.
, 2020
, “Alphred: A Multi-Modal Operations Quadruped Robot for Package Delivery Applications
,” IEEE Rob. Autom. Lett.
, 5
(4
), pp. 5409
–5416
. 3.
Aoyagi
, S.
, Kohama
, A.
, Nakata
, Y.
, Hayano
, Y.
, and Suzuki
, M.
, 2010
, “Improvement of Robot Accuracy by Calibrating Kinematic Model Using a Laser Tracking System-Compensation of Non-Geometric Errors Using Neural Networks and Selection of Optimal Measuring Points Using Genetic Algorithm
,” 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems
, Taipei, Taiwan
, Oct. 18–22
, IEEE, pp. 5660
–5665
.4.
Jiang
, Z.
, Zhou
, W.
, Li
, H.
, Mo
, Y.
, Ni
, W.
, and Huang
, Q.
, 2017
, “A New Kind of Accurate Calibration Method for Robotic Kinematic Parameters Based on the Extended Kalman and Particle Filter Algorithm
,” IEEE Trans. Ind. Electron.
, 65
(4
), pp. 3337
–3345
. 5.
Bai
, Y.
, and Wang
, D.
, 2020
, “Improve 3D Modeless Robots Calibration Accuracy Using a Shallow Neural Network Technique
,” 2020 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM)
, Sorrento, Italy
, June 24–26
, IEEE, pp. 550
–555
.6.
Xu
, Q.
, Nie
, Z.
, Xu
, H.
, Zhou
, H.
, Attar
, H. R.
, Li
, N.
, Xie
, F.
, and Liu
, X.-J.
, 2022
, “Supermeshing: a New Deep Learning Architecture for Increasing the Mesh Density of Physical Fields in Metal Forming Numerical Simulation
,” ASME J. Appl. Mech.
, 89
(1
), p. 011002
. 7.
Jiang
, H.
, Nie
, Z.
, Yeo
, R.
, Farimani
, A. B.
, and Kara
, L. B.
, 2021
, “StressGAN: A Generative Deep Learning Model for Two-Dimensional Stress Distribution Prediction
,” ASME J. Appl. Mech.
, 88
(5
), p. 051005
. 8.
Nie
, Z.
, Lin
, T.
, Jiang
, H.
, and Kara
, L. B.
, 2021
, “TopologyGAN: Topology Optimization Using Generative Adversarial Networks Based on Physical Fields Over the Initial Domain
,” ASME J. Mech. Des.
, 143
(3
), p. 031715
. 9.
Pfaff
, T.
, Fortunato
, M.
, Sanchez-Gonzalez
, A.
, and Battaglia
, P. W.
, 2020
, “Learning Mesh-Based Simulation With Graph Networks
,” preprint arXiv:2010.03409.10.
Xie
, Y.
, Franz
, E.
, Chu
, M.
, and Thuerey
, N.
, 2018
, “tempogan: A Temporally Coherent, Volumetric GAN for Super-Resolution Fluid Flow
,” ACM Trans. Graphics
, 37
(4
), pp. 1
–15
.11.
Jang
, J. H.
, Kim
, S. H.
, and Kwak
, Y. K.
, 2001
, “Calibration of Geometric and Non-Geometric Errors of an Industrial Robot
,” Robotica
, 19
(3
), pp. 311
–321
. 12.
Hasan
, A. T.
, Ismail
, N.
, Hamouda
, A. M. S.
, Aris
, I.
, Marhaban
, M. H.
, and Al-Assadi
, H.
, 2010
, “Artificial Neural Network-Based Kinematics Jacobian Solution for Serial Manipulator Passing Through Singular Configurations
,” Adv. Eng. Softw.
, 41
(2
), pp. 359
–367
. 13.
Strobl
, K. H.
, and Hirzinger
, G.
, 2006
, “Optimal Hand-Eye Calibration
,” 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems
, Beijing, China
, Oct. 9–15
, IEEE, pp. 4647
–4653
.14.
Norgaard
, M.
, Ravn
, O.
, Poulse
, N.
, and Hansen
, L.
, 2001
, Neural Networks for Modelling and Control of Dynamic Systems
. Springer-Verlag London Ltd.
, London, UK
.15.
Bayramoglu
, E.
, Andersen
, N. A.
, Ravn
, O.
, and Poulsen
, N. K.
, 2011
, “Pre-Trained Neural Networks Used for Non-Linear State Estimation
,” 2011 10th International Conference on Machine Learning and Applications and Workshops
, Honolulu, HI
, Dec. 18–21
, Vol. 1
, IEEE, pp. 304
–310
..16.
Nguyen
, H.-N.
, Zhou
, J.
, and Kang
, H.-J.
, 2015
, “A Calibration Method for Enhancing Robot Accuracy Through Integration of an Extended Kalman Filter Algorithm and an Artificial Neural Network
,” Neurocomputing
, 151
(3
), pp. 996
–1005
. 17.
Wang
, Z.
, Chen
, Z.
, Wang
, Y.
, Mao
, C.
, and Hang
, Q.
, 2019
, “A Robot Calibration Method Based on Joint Angle Division and an Artificial Neural Network
,” Math. Problems Eng.
, 2019
(5
), pp. 1
–12
.18.
Cao
, C.-T.
, Do
, V.-P.
, and Lee
, B.-R.
, 2019
, “A Novel Indirect Calibration Approach for Robot Positioning Error Compensation Based on Neural Network and Hand-Eye Vision
,” Appl. Sci.
, 9
(9
), p. 1940
. 19.
Wang
, D.
, Bai
, Y.
, and Zhao
, J.
, 2012
, “Robot Manipulator Calibration Using Neural Network and a Camera-Based Measurement System
,” Trans. Inst. Meas. Control
, 34
(1
), pp. 105
–121
. 20.
Bay
, H.
, Tuytelaars
, T.
, and Gool
, L. V.
, 2008
, “Speeded-Up Robust Features (SURF)
,” Comput. Vis. Image Underst.
, 110
(3
), pp. 346
–359
.21.
Wu
, H.
, Zou
, K.
, Zhang
, T.
, Borst
, A.
, and Kühnlenz
, K.
, 2012
, “Insect-Inspired High-Speed Motion Vision System for Robot Control
,” Biol. Cybernet.
, 106
(8
), pp. 453
–463
. 22.
Smisek
, J.
, Jancosek
, M.
, and Pajdla
, T.
, 2011
, “3d With Kinect
,” 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)
, Barcelona, Spain
, Nov. 6–13
, Springer, pp. 3
–25
.23.
Almusawi
, A. R.
, Dülger
, L. C.
, and Kapucu
, S.
, 2016
, “A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (denso Vp6242)
,” Comput. Intell. Neurosci.
, 2016
, p. 5720163
. 24.
Ogawa
, T.
, and Kanada
, H.
, 2010
, “Solution for Ill-Posed Inverse Kinematics of Robot Arm by Network Inversion
,” J. Rob.
, 2010
, p. 870923
.25.
KöKer
, R.
, 2013
, “A Genetic Algorithm Approach to a Neural-Network-Based Inverse Kinematics Solution of Robotic Manipulators Based on Error Minimization
,” Inf. Sci.
, 222
, pp. 528
–543
.26.
Daya
, B.
, Khawandi
, S.
, and Akoum
, M.
, 2010
, “Applying Neural Network Architecture for Inverse Kinematics Problem in Robotics
,” J. Softw. Eng. Appl.
, 3
(3
), p. 230
. 27.
Bo
, L.
, Wei
, T.
, Zhang
, C.
, Fangfang
, H.
, Guangyu
, C.
, and Yufei
, L.
, 2022
, “Positioning Error Compensation of an Industrial Robot Using Neural Networks and Experimental Study
,” Chin. J. Aeronautics
, 35
(2
), pp. 346
–360
.28.
Zhang
, D.
, Kong
, L.
, Zhang
, S.
, Li
, Q.
, and Fu
, Q.
, 2020
, “Neural Networks-Based Fixed-Time Control for a Robot With Uncertainties and Input Deadzone
,” Neurocomputing
, 390
, pp. 139
–147
. 29.
Lee
, C. G.
, 1982
, “Robot Arm Kinematics, Dynamics, and Control
,” Computer
, 15
(12
), pp. 62
–80
. 30.
Featherstone
, R.
, 1983
, “Position and Velocity Transformations Between Robot End-Effector Coordinates and Joint Angles
,” Int. J. Rob. Res.
, 2
(2
), pp. 35
–45
. 31.
Liu
, Y.
, Wang
, D.
, Sun
, J.
, Chang
, L.
, Ma
, C.
, Ge
, Y.
, and Gao
, L.
, 2015
, “Geometric Approach for Inverse Kinematics Analysis of 6-dof Serial Robot
,” 2015 IEEE International Conference on Information and Automation
, Lijiang, China
, Aug. 8–10
, IEEE, pp. 852
–855
.32.
Karlik
, B.
, and Aydin
, S.
, 2000
, “An Improved Approach to the Solution of Inverse Kinematics Problems for Robot Manipulators
,” Eng. Appl. Artif. Intell.
, 13
(2
), pp. 159
–164
. 33.
Köker
, R.
, 2005
, “Reliability-Based Approach to the Inverse Kinematics Solution of Robots Using Elman’s Networks
,” Eng. Appl. Artif. Intell.
, 18
(6
), pp. 685
–693
.34.
Driscoll
, J. A.
, 2000
, “Comparison of Neural Network Architectures for the Modeling of Robot Inverse Kinematics
,” Proceedings of the IEEE SoutheastCon 2000. 'Preparing for The New Millennium' (Cat. No. 00CH37105)
, Nashville, TN
, Apr. 9
, IEEE, pp. 44
–51
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