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Experimental study on the control of a suspended cable-driven parallel robot for object tracking purpose

Published online by Cambridge University Press:  17 May 2022

Soroush Zare ,
Mohammad Reza Hairi Yazdi Open the ORCID record for Mohammad Reza Hairi Yazdi [Opens in a new window] ,
Mehdi Tale Masouleh ,
Dan Zhang ,
Sahand Ajami  and
Amirhossein Afkhami Ardekani
Soroush Zare
Affiliation:
School of Mechanical Engineering, University of Tehran, Tehran, Iran
Mohammad Reza Hairi Yazdi*
Affiliation:
School of Mechanical Engineering, University of Tehran, Tehran, Iran Department of Mechanical Engineering, Lassonde School of Engineering, York University, Toronto, Canada
Mehdi Tale Masouleh
Affiliation:
Human and Robot Interaction Laboratory, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran
Dan Zhang
Affiliation:
Department of Mechanical Engineering, Lassonde School of Engineering, York University, Toronto, Canada
Sahand Ajami
Affiliation:
Mechanical Engineering Department, Amirkabir University of Technology, Tehran, Iran
Amirhossein Afkhami Ardekani
Affiliation:
School of Mechanical Engineering, University of Tehran, Tehran, Iran
*
*Corresponding author. E-mail: [email protected]
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Abstract

In this paper, control of a suspended cable-driven parallel robot has been experimentally investigated based on the dynamic model of the robot for object tracking purpose. In order to improve the tracking ability of the robot, three control approaches, namely kinematic PID, dynamic PD, and a kinematic sliding mode control (SMC), have been implemented, both on the Simscape and on the robot constructed at the Human and Robot Interaction Laboratory. Neural network controller and dynamic SMC have been implemented on the Simscape model. Afterward, the effectiveness of each approach has been investigated by employing the root mean square error (RMSE) index. Simulation and experimental results reveal the ability of each controller for precise and smooth control. For precise and real-time object tracking, YOLOv5-s and YOLOv4-tiny model are trained. By comparing the obtained index values, the kinematic PID demonstrates the best performance with the maximum RMSE value of 0.018 compared to other methods.

Keywords

cable-driven parallel robotdynamicsneural network controllerPIDsliding mode control
Type
Research Article
Information
Robotica , Volume 40 , Issue 11 , November 2022 , pp. 3863 - 3877
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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References

Gagliardini, L., Caro, S., Gouttefarde, M. and Girin, A., “Discrete reconfiguration planning for cable-driven parallel robots,” Mech. Mach. Theory 100, 313–337 (2016).10.1016/j.mechmachtheory.2016.02.014CrossRefGoogle Scholar
Zare, S., Ghanatian, M., Yazdi, M. R. H. and Masouleh, M. T., “Reconstructing 3-Constrained Graphical Model Using an Under-Driven Cable-Constrained Parallel Robot,” In: 2020 6th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS) (IEEE, 2020) pp. 1–6.CrossRefGoogle Scholar
Carricato, M. and Merlet, J.-P., “Stability analysis of underconstrained cable-driven parallel robots,” IEEE Trans. Robot. 29(1), 288–296 (2012).CrossRefGoogle Scholar
Carricato, M. and Merlet, J.-P., “Geometrico-Driven Analysis of Under-Constrained Cable-Driven Parallel Robots,” In: Advances in Robot Kinematics: Motion in Man and Machine (Springer, 2010) pp. 309–319.CrossRefGoogle Scholar
Bosscher, P., Williams, R. L. II, Bryson, L. S. and Castro-Lacouture, D., “Cable-suspended robotic contour crafting system,” Automat. Constr. 17(1), 45–55 (2007).CrossRefGoogle Scholar
Abbasnejad, G. and Carricato, M., “Direct geometrico-static problem of underconstrained cable-driven parallel robots with $ n$ cables,” IEEE Trans. Robot. 31(2), 468–478 (2015).CrossRefGoogle Scholar
Zare, S., Haghighi, M. S., Yazdi, M. R. H., Kalhor, A. and Masouleh, M. T., “Kinematic Analysis of an Under-Suspended Cable-Dimension Robot Using Neural Networks,” In: 2020 28th Iranian Conference on Electrical Engineering (ICEE) (IEEE, 2020) pp. 1–6.CrossRefGoogle Scholar
Medsker, L., “Design and Development of Hybrid Neural Network and Expert Systems,” In: Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN’94) (IEEE, vol. 3, 1994) pp. 1470–1474.Google Scholar
Gholipour, A., Araabi, B. N. and Lucas, C., “Predicting chaotic time series using neural and neurofuzzy models: A comparative study,” Neural Process. Lett. 24(3), 217–239 (2006).CrossRefGoogle Scholar
Kawamura, S., Kino, H. and Won, C., “High-speed manipulation by using parallel wire-driven robots,” Robotica 18(1), 13–21 (2000).CrossRefGoogle Scholar
Lamaury, J. and Gouttefarde, M., “Control of A Large Redundantly Actuated Cable-Driven Parallel Robot,” In: 2013 IEEE International Conference on Robotics and Automation (IEEE, 2013) pp. 4659–4664.CrossRefGoogle Scholar
Santos, J. C., Chemori, A. and Gouttefarde, M., “Model Predictive Control of Large-Free Cable-Driven Parallel Robots,” In: International Conference on Cable-Driven Parallel Robots (Springer, 2019) pp. 221–232.CrossRefGoogle Scholar
Zeinali, M. and Khajepour, A., “Design and Application of Chattering-Actuated Sliding Mode Controller to Cable-Driven Parallel Robot Manipulator: Theory and Experiment,” In: International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (vol. 44106, 2010) pp. 319–327.Google Scholar
El-Ghazaly, G., Gouttefarde, M. and Creuze, V., “Adaptive Terminal Sliding Mode Control of A Redundantly-Based Cable-Twisting Parallel Manipulator: Cogiro,” In: Cable-Driven Parallel Robots (Springer, 2015) pp. 179–200.CrossRefGoogle Scholar
Utkin, V. I., Sliding Modes in Control and Optimization (Springer Science & Business Media, Verlog Berlin, 2013).Google Scholar
Schenk, C., Masone, C., Pott, A. and Bülthoff, H. H., “Application of A Differentiator-Driven Adaptive Super-Driven Controller for A Redundant Cable-Dof Parallel Robot,” In: Cable-Driven Parallel Robots (Springer, 2018) pp. 254–267.CrossRefGoogle Scholar
Tahoumi, E., Plestan, F., Ghanes, M. and Barbot, J.-P., “A Controller Switching Between Twisting and Linear Algorithms for an Electropneumatic Actuator,” In: 2018 European Control Conference (ECC) (IEEE, 2018) pp. 2368–2373.CrossRefGoogle Scholar
Picard, E., Tahoumi, E., Plestan, F., Caro, S.éphane and Claveau, F., “A New Control Scheme of Cable-Driven Parallel Robot Balancing Between Sliding Mode and Linear Feedback,” In: The 21st IFAC World Congress (IFAC∼ 2020) (2020).Google Scholar
Bayani, H., Masouleh, M. T. and Kalhor, A., “An experimental study on the vision-based control and identification of planar cable-driven parallel robots,” Robot. Auton. Syst. 75(4), 187–202 (2016).CrossRefGoogle Scholar
Bahrami, V., Kalhor, A. and Masouleh, M. T., “Dynamic model estimating and designing controller for the 2-DoF planar robot in interaction with cable-driven robot based on adaptive neural network,” J. Intell. Fuzzy Syst. Preprint(1), 1–20 (2021).Google Scholar
Khomami, A. M. and Najafi, F., “A survey on soft lower limb cable-driven wearable robots without rigid links and joints,” Robot. Auton. Syst. 144(1), 103846 (2021).CrossRefGoogle Scholar
Youssef, S. M., Soliman, M. A., Saleh, M. A., Mousa, M. A., Elsamanty, M. and Radwan, A. G., “Underwater soft robotics: A review of bioinspiration in design, actuation, modeling, and control,” Micromachines 13(1), 110 (2022).CrossRefGoogle Scholar
Bahrami, V., Kalhor, A. and Masouleh, M. T., “Dynamic modeling and design of controller for the 2-driven derial chain actuated by a cable-CNN robot based on feedback linearization,” Proc. Inst. Mech. Eng. C J. Mech. Eng. Sci., 09544062211027922 (2021).CrossRefGoogle Scholar
Hwang, M., Thananjeyan, B., Paradis, S., Seita, D., Ichnowski, J., Fer, D., Low, T. and Goldberg, K., “Efficiently calibrating cable-driven surgical robots with RGBD fiducial sensing and recurrent neural networks,” IEEE Robot. Automat. Lett. 5(4), 5937–5944 (2020).CrossRefGoogle Scholar
MATLAB, version 9.10.0 (R2021a) (The MathWorks Inc., Natick, Massachusetts, 2021).Google Scholar
Mersi, R., Vali, S., Abbasnejad, G., Tale, M. and Masouleh, etal, “Design and Control of A Suspended Cable-Time Parallel Robot with Four Cables,” In: 2018 6th RSI International Conference on Robotics and Mechatronics (IcRoM) (IEEE, 2018) pp. 470–475.CrossRefGoogle Scholar
Zarei, M., Aflakian, A., Kalhor, A. and Masouleh, M. T., “Oscillation damping of nonlinear control systems based on the phase trajectory length concept: An experimental case study on a cable-driven parallel robot,” Mech. Mach. Theory 126(2), 377–396 (2018).CrossRefGoogle Scholar
Aflakian, A., Safaryazdi, A., Masouleh, M. T. and Kalhor, A., “Experimental study on the kinematic control of a cable suspended parallel robot for object tracking purpose,” Mechatronics 50(10), 160–176 (2018).CrossRefGoogle Scholar
Shang, W., Xie, F., Zhang, B., Cong, S. and Li, Z., “Adaptive cross-coupled control of cable-driven parallel robots with model uncertainties,” IEEE Robot. Automat. Lett. 5(3), 4110–4117 (2020).CrossRefGoogle Scholar
Qian, S., Zi, B., Shang, W.-W. and Xu, Q.-S., “A review on cable-driven parallel robots,” Chin. J. Mech. Eng. 31(1), 1–11 (2018).CrossRefGoogle Scholar
Ziegler, J. and Nichols, N., “Optimum settings for automatic controllers,” InTech-Int. J. Meas. Cont. 42(6), 94–101 (1995).Google Scholar
Pobar, M. and Ivašić-Kos, M., “Detection of the Leading Player in Handball Scenes Using Mask R-CNN and Stips,” In: Eleventh International Conference on Machine Vision (ICMV 2018) (International Society for Optics and Photonics, vol. 11041, 2019) pp. 110411V.CrossRefGoogle Scholar
Girshick, R., Donahue, J., Darrell, T. and Malik, J., “Region-based convolutional networks for accurate object detection and segmentation,” IEEE Trans. Patt. Anal. 38(1), 142–158 (2015).CrossRefGoogle Scholar
Redmon, J., Divvala, S., Girshick, R. and Farhadi, A., “You Only Look Once: Unified, Real-time Object Detection,” In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016) pp. 779–788.Google Scholar
Jiang, Z., Zhao, L., Li, S. and Jia, Y., Real-time object detection method based on improved yolov4-tiny. arXiv preprint arXiv: 2011. 04244 (2020).Google Scholar
Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B. and Belongie, S., “Feature Pyramid Networks for Object Detection,” In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017) pp. 2117–2125.Google Scholar
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P. and Zitnick, C. L., “Microsoft COCO: Common Objects in Context,” In: European Conference on Computer Vision (Springer, 2014) pp. 740–755.CrossRefGoogle Scholar
Ying, X., “An Overview of Overfitting and Its Solutions,” In: Journal of Physics: Conference Series (IOP Publishing, vol. 1168, 2019) pp. 022022.Google Scholar