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Determination method of limit interpolation points in industrial robot control system

Published online by Cambridge University Press:  04 April 2025

Zhenyang Lv Open the ORCID record for Zhenyang Lv [Opens in a new window] ,
Yi Wang ,
Guangpeng Zhang  and
Xionghui Wang
Zhenyang Lv
Affiliation:
School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an, Shaanxi, People’s Republic of China
Yi Wang
Affiliation:
School of Art and Design, Xi’an University of Technology, Xi’an, Shaanxi, People’s Republic of China
Guangpeng Zhang*
Affiliation:
School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an, Shaanxi, People’s Republic of China
Xionghui Wang
Affiliation:
School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an, Shaanxi, People’s Republic of China
*
Corresponding author: Guangpeng Zhang; Email: [email protected]
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Abstract

With advancements in industrial robot technology and the ongoing enhancements in control system performance, the demand for precise robot motion is increasing. Generally, an increased number of interpolation points enhances the precision of robot movement, but excessive points can lead to jittering and out-of-step issues. This paper investigates the relationship between the number of motion interpolation points and the response times of the control system and the robot’s terminal velocity, based on the theoretical calculation and experimental analysis of the limit interpolation points for the control system of a self-developed 6-DOF (Six Degree of Freedom) robot. The method for calculating limit interpolation points is refined using the least squares method, and equations are derived for different control system response time and robot’s terminal velocity reaction times. The validity of the prediction curves is verified through experimental analysis.

Keywords

industrial robotbeckhoff controllerarc interpolationlimit interpolation points
Type
Research Article
Information
Robotica , First View , pp. 1 - 15
Copyright
© The Author(s), 2025. Published by Cambridge University Press

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References

Maghami, A. and Khoshdarregi, M., “Vision-based target localization and online error correction for high-precision robotic drilling,” Robotica (2024). doi: 10.1017/S0263574724001255.CrossRefGoogle Scholar
Moghaddam, E. S., Saryazdi, M. G. and Taghvaeipour, A., “Trajectory optimization of a spot-welding robot in the joint and Cartesian spaces,” Int. J. Robot. Autom. 38(2), 109–125 (2023). doi:10.2316/J.2023.206-0701.Google Scholar
Kapsalyamov, A., Hussain, S., Goecke, R., Brown, N. A. T. and Jamwal, P. K., “Customized stiffness control strategy for a six-bar linkage-based gait rehabilitation robot,” Robotica, 1–18 (2024). doi:10.1017/S0263574724001425.Google Scholar
Shanmugasundar, G., Sapkota, G., Čep, R. and Kalita, K., “Application of MEREC in multi-criteria selection of optimal spray-painting robot,” Processes 10(6), 1172 (2022). doi: 10.3390/pr10061172.Google Scholar
Liu, Y., Guo, S., Yin, Y. Jiang, Z. and Liu, T., “Design and compliant control of a piggyback transfer robot,” J. Mech. Robot. 14(3), 031009 (2022). doi: 10.1115/1.4052700.CrossRefGoogle Scholar
Jin, X., Chen, K. K., Zhao, Y. Ji, J. and Jing, P., “Simulation of hydraulic transplanting robot control system based on fuzzy PID controller,” Measurement 164, 108023 (2020). doi: 10.1016/j.measurement.2020.108023.CrossRefGoogle Scholar
Domae, Y., “Recent trends in the research of industrial robots and future outlook,” J. Robot. Mechatron. 31(1), 57–62 (2019). doi: 10.20965/jrm.2019.p0057.CrossRefGoogle Scholar
Tepper, C., Matei, A., Zarges, J. Ulbrich, S. and Weigold, M., “Optimal design for compliance modeling of industrial robots with Bayesian inference of stiffnesses,” Prod. Eng. 17(5), 643–651 (2023). doi: 10.1007/s11740-023-01198-3.CrossRefGoogle Scholar
Clark, A. and Ahmad, I., “Touchless and nonverbal human-robot interfaces: An overview of the state-of-the-art,” Smart Health 27, 100365 (2023). doi:10.1016/j.smhl.2022.100365.CrossRefGoogle Scholar
Sun, P., Liu, Q., Ding, J. and Pi, S., "Open CNC System Design for Multiple Intelligent Functions Based on Twincat and Net Framework," IEEE International Conference on Mechatronics and Automation (ICMA), Takamatsu, Japan (2017) pp. 910–915. doi:10.1109/ICMA.2017.8015937.CrossRefGoogle Scholar
Liang, C. J., McGee, W., Menassa, C. C. and Kamat, V. R., “Real-time state synchronization between physical construction robots and process-level digital twins,” Construct. Rob. 6(1), 57–73 (2022). doi:10.1007/s41693-022-00068-1.CrossRefGoogle Scholar
Feng, Z., Sun, T. and Tian, X., "Research on SCARA Robot Sorting System Based on CoDeSys," Chinese Control and Decision Conference (CCDC), Nanchang, China (2019) pp. 5611–5615.Google Scholar
Hijazi, A., Brethé, J.-F. and Lefebvre, D., “Design of an XY-theta platform held by a planar manipulator with four revolute joints and evaluation of its precision performances,” Robotica 34(11), 2532–2545 (2016). doi:10.1017/S0263574715000193.CrossRefGoogle Scholar
Yang, S., Wang, S. and Huang, S., “ROS-based remote control of industrial robot joystick,” J. Mech. Eng. Sci. 237(1), 160–169 (2023). doi: 10.1177/09544062221115549.CrossRefGoogle Scholar
Eugster, M., Merlet, J.-P., Gerig, N., Cattin, P. C. and Rauter, G., “Miniature parallel robot with submillimeter positioning accuracy for minimally invasive laser osteotomy,” Robotica 40(4), 1070–1097 (2022). doi:10.1017/S0263574721000990.CrossRefGoogle Scholar
Zhao, J., Liu, S. and Li, J., “Research and implementation of autonomous navigation for mobile robots based on SLAM algorithm under ROS,” Ah S Sens. 22(11), 4172 (2022). doi:10.3390/s22114172.Google ScholarPubMed
Zhang, J., Meng, W. and Yin, Y., “Design of a decentralized internet-based control system for industrial robot robotic arms,” Appl. Math. Nonlinear Sci. 9(1) (2024). doi: 10.2478/amns.2023.1.00100.Google Scholar
Zhou, J., Li, Z., Lv, G. and Liu, E., “Fuzzy logic-based adaptive tracking control of manipulator actuated by dc motor,” Int. J. ROB. Autom. 36(3), 196–203 (2021). doi:10.2316/J.2021.206-0526.Google Scholar
Tamizi, M. G., Yaghoubi, M. and H.Najjaran, H., “A review of recent trend in motion planning of industrial robots,” Int. J. Intell. Robot. 7(2), 253–274 (2023). doi:10.1007/s41315-023-00274-2.Google Scholar
Yan, N., Chen, G., Wu, J. and Jin, G., “Trajectory tracking and compensation control of redundant manipulator based on integratedcontroller,” Int. J. Robot. Autom. 37(4), 362–371 (2022). doi: 10.2316/J.2022.206-0762.Google Scholar
Mañas-Álvarez, F. J., Guinaldo, M., Dormido, R. and Dormido, S., “Robotic park: Multi-agent platform for teaching control and robotics,” IEEE Access 11, 34899–34911 (2023). doi:10.1109/ACCESS.2023.3264508.Google Scholar
Tourajizadeh, H. and Gholami, O., “Closed loop nonlinear optimal control of a 3PRS parallel robot,” Int. J. Robot. Autom. 36(2), 128–132 (2021). doi:10.2316/J.2021.206-0401.Google Scholar
Chotikunnan, R., Roongprasert, K., Chotikunnan, P. Imura, P., Sangworasil, M. and Srisiriwat, A., “Robotic arm design and control using MATLAB/Simulink,” Int. J. Membtance Sci. Technol. 10(3), 2448–2459. doi:10.15379/ijmst.v10i3.1974.Google Scholar
Whitman, J., Travers, M. and Choset, H., “Learning modular robot control policies,” IEEE Trans. Robot. 39(5), 4095–4113 (2023). doi:10.1109/TRO.2023.3284362.Google Scholar
Xiao, W. L., Zhang, K. Y., Wang, S. P. Xiao, J. H., Xing, H. W., Li, R. P. and Zhao, G., “STEP-NC enabled edge-cloud collaborative manufacturing system for compliant CNC machining,” J. Manuf. Syst. 72, 460–474 (2024). doi:10.1016/j.jmsy.2023.12.005.Google Scholar
Zu, H., Zhao, M., Chen, Z. He, J. and Zhou, J., "Measurement and Analysis of End Jitter of Six-Axis Industrial Robots," International Conference on Intelligent Robotics and Applications (ICRIA) (Springer Nature Singapore, Singapore, 2023) pp. 101–109. doi:10.1007/978-981-99-6504-5_9.Google Scholar
Li, J., Ma, H., Zhang, X. Wu, X., Chen, Z., Li, X., Li, N. and Cheng, Z., "Measurement and Application of Industrial Robot Jitter," International Conference on Intelligent Robotics and Applications(ICRIA) (Springer Nature Singapore, Singapore, 2023) pp. 371–380.Google Scholar
Li, R., Ding, N., Zhao, Y. and Liu, H., “Real-time trajectory position error compensation technology of industrial robot,” Measurement 208, 112418 (2023). doi:10.1016/j.measurement.2022.112418 CrossRefGoogle Scholar
Hughes, N., Chang, Y., Hu, S. Talak, R., Abdulhai, R., Strader, J. and Carlone, L., “Foundations of spatial perception for robotics: Hierarchical representations and real-time systems,” Ind. Robot. 02783649241229725 (2024). doi:10.1177/0278364924122972.Google Scholar
Laurenzi, A., Antonucci, D., Tsagarakis, N. G. and Muratore, L., “The xbot2 real-time middleware for robotics,” Robot. Auton. Syst. 163, 104379 (2023). doi:10.1016/j.robot.2023.104379.Google Scholar