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Design and Development of a Novel 2-Degree-of-Freedom Parallel Robot

Published online by Cambridge University Press:  10 April 2019

Changxi Cheng
Affiliation:
School of Mechanical and Electric Engineering, Guangzhou University, Guangzhou 510006, P.R.China. E-mails: 237997968@qq.com, nhzcl@163.com
Wenkai Huang*
Affiliation:
School of Mechanical and Electric Engineering, Guangzhou University, Guangzhou 510006, P.R.China. E-mails: 237997968@qq.com, nhzcl@163.com Center for Research on Leading Technology of Special Equipment, School of Mechanical and Electric Engineering, Guangzhou University, Guangzhou 510006, P.R.China
Chunliang Zhang
Affiliation:
School of Mechanical and Electric Engineering, Guangzhou University, Guangzhou 510006, P.R.China. E-mails: 237997968@qq.com, nhzcl@163.com
*
*Corresponding author. E-mail: 16796796@qq.com

Summary

Parallel robots are widely used in the fields of manufacturing, medical science, education, scientific research, etc. Many studies have been conducted on the topic already. However, shortcomings still exist, especially in certain situations. To meet the demand of good speed and load performances at the same time, this work presents a novel 2-degree-of-freedom parallel robot. The structural design, static, stiffness, and reachable workspace analysis of the robot are given in the manuscript. Experiment regarding the accuracy and speed performance is conducted, and the results are provided. In the end, potential applications of the proposed robot are suggested.

Type
Articles
Copyright
© Cambridge University Press 2019 

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References

He, W., Chen, Y. and Yin, Z., “Adaptive neural network control of an uncertain robot with full-state constraints,” IEEE Trans. Cybern. 46(3), 620629 (2016). doi: 10.1109/TCYB.2015.2411285.CrossRefGoogle ScholarPubMed
He, W., Dong, Y. and Sun, C., “Adaptive neural impedance control of a robotic manipulator with input saturation,” IEEE Trans. Syst. Man Cybern. Syst. 46(3), 334344. (2016). doi: 10.1109/TSMC.2015.2429555.CrossRefGoogle Scholar
Yang, C., Huang, K., Cheng, H., Li, Y. and Su, C.-Y., “Haptic identification by ELM controlled uncertain manipulator,” IEEE Trans. Syst. Man Cybern. Syst. 47(8), 23982409 (2017). doi: 10.1109/TSMC.2017.2676022.CrossRefGoogle Scholar
Yang, C., Wang, X., Li, Z., Li, Y. and Su, C.-Y., “Teleoperation control based on combination of wave variable and neural networks,” IEEE Trans. Syst. Man Cybern. Syst. 47(8), 21252136 (2017). doi: 10.1109/TSMC.2016.2615061.CrossRefGoogle Scholar
Lei, C., Isaac, A. and Allison, G., “Design and development of a five-bar robot for research into lower extremity proprioception,” Robotica 36(2), 298311 (2018). doi: 10.1017/S0263574717000406.Google Scholar
Mir-Nasiri, N., “Design, modelling and control of four-axis parallel robotic arm for assembly operations,” Assembly Autom. 24(4), 365369 (2004). doi: 10.1108/01445150410565261.CrossRefGoogle Scholar
Ulrich, M. and Steger, C.. “Hand-eye calibration of SCARA robots using dual quaternions,” Russ. Sci. Citation Index 26(1), 231231 (2016). doi: 10.1134/S1054661816010272.Google Scholar
Urrea, C. and Kern, J.. “Trajectory tracking control of a real redundant manipulator of the SCARA type,” J. Electr. Eng. Tech. 11(1), 215226 (2016). doi: 10.5370/JEET.2016.11.1.215.CrossRefGoogle Scholar
Mosquera, L., Víctor, H., Vivas, A. and Óscar, A., “Control backstepping de un robot SCARA con incertidumbre paramétrica [A backstepping control for SCARA robot based on parametric uncertainty],” Cienc. Ing. Neogranadina 22(1), 107122 (2012).10.18359/rcin.252CrossRefGoogle Scholar
Yi, C. X., “A compensation control method using neural network for mechanical deflection error in SCARA robot with random payload,” J. Korean Soc. Mech. Technol. 13(3), 716 (2011). doi: 10.17958/ksmt.13.3.201109.7.Google Scholar
Rossomando, F. G. and Soria, C. M., “Discrete-time sliding mode neuro-adaptive controller for SCARA robot arm,” J. Neural Comput. Appl. 28(12), 38373850 (2017). doi: 10.1007/s00521-016-2242-7.CrossRefGoogle Scholar
Wang, J. S. and Pritschow, G., “Kinematics, singularity and workspace of planar 5R symmetrical parallel mechanisms,” J. Mech. Mach. Theory 41(2), 145169 (2006). doi: 10.1016/j.mechmachtheory.2005.05.004.Google Scholar
Sun, T., Liang, D. and Song, Y.. “Singular-perturbation-based nonlinear hybrid control of redundant parallel robot,” IEEE Trans. Ind. Electron. 64(4), 33263336 (2018). doi: 10.1109/TIE.2017.2756587.CrossRefGoogle Scholar
Liang, D., Song, Y., Sun, T. and Jin, X., “Dynamic modeling and hierarchical compound control of a novel 2-DOF flexible parallel manipulator with multiple actuation modes,” J. Mechanical Systems and Signal Processing 103, 413439 (2018). doi: 1016/j.ymssp.2017.10.004.CrossRefGoogle Scholar
Liu, X., Wang, J. and Gao, F., “Performance atlases of the workspace for planar 3-DOF parallel manipulators,” Robotica 18(5), 563568 (2000). doi: 10.1017/S0263574700002678.CrossRefGoogle Scholar
Briot, S. and Bonev, I. A., “Accuracy analysis of 3-DOF planar parallel robots,” Mech. Mach. Theory 43(4), 445458 (2008). doi: 10.1016/j.mechmachtheory.2007.04.002.CrossRefGoogle Scholar
Huang, M. Z. and Thebert, J. L., “A study of workspace and singularity characteristics for design of 3-DOF planar parallel robots,” Int. J. Adv. Manuf. Tech. 51(5), 789797 (2010). doi: 10.1007/s00170-010-2632-4.CrossRefGoogle Scholar
Yang, Y., Tian, Y., Peng, Y. and Pu, H., “A novel 2-DOF planar translational mechanism composed by scissor-like elements,” Mech. Sci. 8(1), 179193 (2017). doi: 10.5194/ms-8-179-2017.CrossRefGoogle Scholar
Jiang, Y., Tiemin, L., Liping, W. and Feifan, C., “Improving tracking accuracy of a novel 3-DOF redundant planar parallel kinematic machine,” Mech. Mach. Theory 119(1), 198218 (2018). doi: 10.1016/j.mechmachtheory.2017.09.012.CrossRefGoogle Scholar
Mejia, L., Simas, H. and Martins, D., “Force capability in general 3 DoF planar mechanisms,” Mech. Mach. Theory 91(1), 120134 (2015). doi: 10.1016/j.mechmachtheory.2015.04.013.CrossRefGoogle Scholar
Kuo, Y.-L. and Huang, P.-Y., “Experimental and simulation studies of motion control of a Delta robot using a model-based approach,” Int. J. Adv. Rob. Syst. 14(6), 114 (2017). doi: 10.1177/1729881417738738.Google Scholar
Kelaiaia, R., “Improving the pose accuracy of the Delta robot in machining operations,” Int. J. Adv. Manuf. Tech 91(5), 22052215 (2017). doi: 10.1007/s00170-016-9955-8.CrossRefGoogle Scholar
Shareef, Z., Just, V., Teichrieb, H. and Traechtler, A., “Design and control of cooperative ball juggling DELTA robots without visual guidance,” Robotica 35(2): 384400 (2017). doi: 10.1017/S0263574715000569.CrossRefGoogle Scholar
Yang, E. C.-Y., Yen, M.-H., Chou, M.-T., Huang, Z.-J. and Chen, S.-K., “Non-spherical ballsocket joint design for Delta-type robots elements,” Mechatronics 45(1), 1424 (2017). doi: 10.1016/j.mechatronics.2017.05.003.CrossRefGoogle Scholar
Yi, C. X., “Optimal design of the second arm of a SCARA robot based on performance evaluation,” J. Korean Soc. Mech. Technol. 11(2), 18 (2009). doi: 10.17958/ksmt.11.2.200906.1.Google Scholar
Hiroaki, S., Yoshitsugu, K. and Masatoshi, H., “SCARA type robot arm with mechanically adjustable compliant joints,” IEEE Conf. Emerging Technol. Factory Autom. Vols. 1–3, 11751181 (2006). doi: 10.1109/ETFA.2006.355192.Google Scholar
Chen, S., Optimization Design and Load Checking of SCARA Robot [D] (South China University of Technology, Guangdong, China, 2015).Google Scholar
Seiko Epson Corporation Japan. Epson Robots G20 [EB/OL], https://global.epson.com/products/robots/products/scara/g.html.Google Scholar
He, W. and Ge, S. S., “Cooperative control of a nonuniform gantry crane with constrained tension,” Automatica 66(4), 146154 (2016). doi: 10.1016/j.automatica.2015.12.026.CrossRefGoogle Scholar
Dai, S.-L., He, S., Lin, H. and Wang, C., “Platoon formation control with prescribed performance guarantees for USVs,” IEEE Trans. Ind. Electron. 65(5), 42374246 (2018). doi: 10.1109/TIE.2017.2758743.CrossRefGoogle Scholar
He, W., Meng, T., He, X. and Ge, S. S., “Unified iterative learning control for flexible structures with input constraints,” Automatica 86, 326336 (2018). doi: 10.1016/j.automatica.2018.06.051.CrossRefGoogle Scholar
He, X. and Zhao, Z., “Boundary control design for a vibrating flexible string system with input nonlinearities,” Nonlinear Dyn. 93(2), 323333 (2018). doi: 10.1007/s11071-018-4194-1.CrossRefGoogle Scholar
He, W. and Dong, Y., “Adaptive fuzzy neural network control for a constrained robot using impedance learning,” IEEE Trans. Neural Networks Learn. Syst. 29(4), 11741186 (2018). doi: 10.1109/TNNLS.2017.2665581.CrossRefGoogle ScholarPubMed
Yang, C., Zeng, C., Liang, P., Li, Z., Li, R. and Su, C.-Y., “Interface design of a physical human robot interaction system for human impedance adaptive skill transfer”, IEEE Trans. Autom. Sci. Eng. 15(1), 329340 (January 2018). doi: 10.1109/TASE.2017.2743000.CrossRefGoogle Scholar
Wang, J.-L., Zhang, X.-X., Wu, H.-N., Huang, T. and Wang, Q., “Finite-time passivity and synchronization of coupled reaction-diffusion neural networks with multiple weights,” IEEE Trans. Cybern. 1–13 (2018). doi: 10.1109/TCYB.2018.2842437.Google Scholar
Yang, C., Jiang, Y., Li, Z., He, W. and Su, C.-Y., “Neural control of bimanual robots with guaranteed global stability and motion precision,” IEEE Trans. Ind. Inf. 13(3), 11621171 (2017). doi: 10.1109/TII.2016.2612646.CrossRefGoogle Scholar
Estun Automation Company China. SCARA Robots [EB/OL], http://c.gongkong.com/ESTUN/p89987.html, 2012/2018-02-11Google Scholar
Hui Tianwei Company China. HORI 3D Printer [EB/OL], http://www.hori3d.com/Printer3D/Info/c99b48c7-84da-473b-b798-5aed0ba774b6. 2015/2018-02-11Google Scholar
Wang, J.-L., Wu, H.-N., Huang, T. and Xu, M., “Output synchronization in coupled neural networks with and without external disturbances,” IEEE Trans. Control Network Syst. 5(4), 20492061 (2018). doi: 10.1109/TCNS.2017.2782488.CrossRefGoogle Scholar
Dai, S.-L., Wang, C. and Wang, M., “Dynamic learning from adaptive neural network control of a class of nonaffine nonlinear systems,” IEEE Trans. Neural Networks Learn. Syst. 25(1), 111123 (2014). doi: 10.1109/TNNLS.2013.2257843.Google ScholarPubMed
Zhao, Z., Shi, J., Lan, X., Wang, X. and Yang, J., “Adaptive neural network control of a flexible string system with non-symmetric dead-zone and output constraint,” Neurocomputing 283, 18 (2018). doi: 10.1016/j.neucom.2017.12.013.CrossRefGoogle Scholar
He, W., He, X., Zou, M. and Li, H., “PDE model based boundary control design for a flexible robotic manipulator with input backlash,” IEEE Trans. Control Syst. Technol. 27(2), 790797 (2018). doi: 10.1109/TCST.2017.2780055.CrossRefGoogle Scholar
He, S., Ai, Q., Ren, C., Dong, J. and Liu, F., “Finite-time resilient controller design of a class of uncertain nonlinear systems with time-delays under asynchronous switching,” IEEE Trans. Syst. Man Cybern. Syst. 49(2), 281286 (2019). doi: 10.1109/TSMC.2018.2798644.CrossRefGoogle Scholar
He, W., Ouyang, Y. and Hong, J., “Vibration control of a flexible robotic manipulator in the presence of input deadzone,” IEEE Trans. Ind. Inf. 13(1), 4859 (2017). doi: 10.1109/TII.2016.2608739.CrossRefGoogle Scholar