Hostname: page-component-848d4c4894-hfldf Total loading time: 0 Render date: 2024-04-30T11:08:59.064Z Has data issue: false hasContentIssue false

Kinematic calibration and feedforward control of a heavy-load manipulator using parameters optimization by an ant colony algorithm

Published online by Cambridge University Press:  04 January 2024

Xinpei Wang
Affiliation:
School of Mechatronics Engineering and Automation, Foshan University, Foshan, 528225, China
Lingbo Xie
Affiliation:
School of Mechatronics Engineering and Automation, Foshan University, Foshan, 528225, China
Mian Jiang*
Affiliation:
School of Mechatronics Engineering and Automation, Foshan University, Foshan, 528225, China
Kuanfang He
Affiliation:
School of Mechatronics Engineering and Automation, Foshan University, Foshan, 528225, China
Yong Chen
Affiliation:
School of Mechatronics Engineering and Automation, Foshan University, Foshan, 528225, China
*
Corresponding author: Mian Jiang; Email: mjiang@fosu.edu.cn

Abstract

Most of the currently available three-degree-of-freedom manipulators are light load and cannot achieve full continuous rotation; given this, we designed a heavy-load manipulator that achieves unrestricted and continuous rotation. Due to manufacturing and assembly errors, parameter deviations between the real manipulator and its underlying theoretical model were unavoidable. Because of the lack of high-precision, high-frequency, and real-time closed-loop detection methods, we proposed a type of kinematics calibration of parameterized ant colony optimization and feedforward control methods. This was done to achieve high-precision motion control. First, an error model combining structural parameters and joint output angles was established, and the global sensitivity of each error source was analyzed to distinguish both primary and secondary sources. Based on the measured data of a laser tracker, the ant colony optimization was then used to identify six error sources. This resulted in both link length and joint driving errors of the designed manipulator. As it is a type of systematic error, the rounding error of the theoretical trajectory was carefully analyzed, and feedforward control methods with different coefficients were designed to further improve positioning accuracy based on the kinematic calibration. Experimental results showed that the proposed kinematic calibration and feedforward control methods achieved relatively precise motion control for the designed manipulator.

Type
Research Article
Copyright
© The Author(s), 2024. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Rubio, J. D. J., “Bat algorithm based control to decrease the control energy consumption and modified bat algorithm based control to increase the trajectory tracking accuracy in robots,” Neural Netw. 161, 437448 (2023).Google Scholar
Rubio, J. D. J., Orozco, E., Cordova, D. A., Islas, M. A., Pacheco, J., Juliana, G., Zacarias, A., Soriano, L. A., Meda-Campana, J. A. and Mujica-Vargas, D., “Modified linear technique for the controllability and observability of robotic arms,” IEEE Access 10, 33663377 (2022).Google Scholar
Lughofer, E. and Skrjanc, I., “Evolving error feedback fuzzy model for improved robustness under measurement noise,” IEEE Trans. Fuzzy Syst. 31(3), 9971008 (2023).Google Scholar
Balcazar, R., Rubio, J. D. J., Orozco, E., Córdova, D. A., Ochoa, G., García, E., Pacheco, J., Gutierrez, G. J., Mújica-Vargas, D. and Aguilar-Ibáñez, C., “The regulation of an electric oven and an inverted pendulum,” Symmetry 14(4), 759 (2022).CrossRefGoogle Scholar
Soriano, L. A., Zamora, E., Vazquez-Nicolas, J. M., Hernández, G., Madrigal, J. A. B. and Balderas, D., “PD control compensation based on a cascade neural network applied to a robot manipulator,” Front. Neurorobot. 14, 577749 (2020).Google Scholar
Silva-Ortigoza, R., Hernandez-Marquez, E., Roldan-Caballero, A., Tavera-Mosqueda, S. and Silva-Ortigoza, G., “Sensorless tracking control for a full-bridge Buck inverter-DC motor system: Passivity and flatness-based design,” IEEE Access 9, 132191132204 (2021).Google Scholar
Joubair, A., Slamani, M. and Bonev, I. A., “Kinematic calibration of a 3-DOF planar parallel robot,” Indus. Robot Int. J. 39(4), 392400 (2012).Google Scholar
Du, G., Zhang, P. and Li, D., “Online robot calibration based on hybrid sensors using kalman filters,” Robot. Comput. Integr. Manuf. 31, 91100 (2015).Google Scholar
Nguyen, H.-N., Zhou, J. and Kang, H.-J., “A calibration method for enhancing robot accuracy through integration of an extended kalman filter algorithm and an artificial neural network,” Neurocomputing 151, 9961005 (2015).CrossRefGoogle Scholar
Klimchik, A., Caro, S. and Pashkevich, A., “Optimal pose selection for calibration of planar anthropomorphic manipulators,” Precis. Eng. 40, 214229 (2015).Google Scholar
Joubair, A. and Bonev, I. A., “Kinematic calibration of a six-axis serial robot using distance and sphere constraints,” Int. J. Adv. Manuf. Technol. 77(1), 515523 (2015).Google Scholar
Huang, T., Whitehouse, D. J. and Chetwynd, D. G., “A unified error model for tolerance design assembly and error compensation of 3-DOF parallel kinematic machines with parallelogram struts,” CIRP Ann. 51(1), 297301 (2002).Google Scholar
Li, Z., Li, S. and Luo, X., “An overview of calibration technology of industrial robots,” IEEE CAA J. Autom. Sin. 8(1), 2336 (2021).Google Scholar
Liu, Y., Zhuang, Z. and Li, Y., “Closed-loop kinematic calibration of robots using a six-point measuring device,” IEEE Trans. Instrum. Meas. 71, 112 (2022).Google Scholar
Wang, S., Jia, Q., Chen, G. and Liu, D., “Complete relative pose error model for robot calibration,” Ind. Robot J. Robot. Res. Appl. 46(5), 622630 (2019).Google Scholar
Chen, T., Lin, J., Wu, D. and Wu, H., “Research of calibration method for industrial robot based on error model of position,” Appl. Sci. 11(3), 1287 (2021).Google Scholar
Nguyen, H.-N., Le, P.-N. and Kang, H.-J., “A new calibration method for enhancing robot position accuracy by combining a robot model-based identification approach and an artificial neural network-based error compensation technique,” Adv. Mech. Eng. 11(1), 168781401882293 (2019).Google Scholar
Ye, H., Wu., J. and Wang, D., “A general approach for geometric error modeling of over-constrained hybrid robot,” Mech. Mach. Theory 176, 104998 (2022).Google Scholar
Xiao, B., Alamdar, A., Song, K., Ebrahimi, A., Gehlbach, P., Taylor, R. H. and Iordachita, I., “Delta robot kinematic calibration for precise robot-assisted retinal surgery,” In: International Symposium on Medical Robotics (ISMR) (2022) pp. 17.Google Scholar
Kamali, K. and Bonev, I. A., “Optimal experiment design for elasto-geometrical calibration of industrial robots,” IEEE/ASME Trans. Mechatron. 24(6), 27332744 (2019).Google Scholar
Filion, A., Joubair, A., Tahan, A. S. and Bonev, I. A., “Robot calibration using a portable photogram-metry system,” Robot. Comput. Integr. Manuf. 49, 7787 (2018).Google Scholar
Lou, Z., Zhang, J., Gao, R., Xu, L., Fan, K.-C. and Wang, X., “A 3D passive laser tracker for accuracy calibration of robots,” IEEE/ASME Trans. Mechatron. 27(6), 58035811 (2022).Google Scholar
Alam, M. M., Ibaraki, S., Fukuda, K., Morita, S., Usuki, H., Otsuki, N. and Yoshioka, H., “Inclusion for static volumetric error compensation,” IEEE/ASME Trans. Mechatron. 27(6), 43394349 (2022).Google Scholar
Lu, B., Li, B., Dou, Q. and Liu, Y., “A unified monocular camera-based and patten-free hand-to-eye calibration algorithm for surgical robots with rcm constraints,” IEEE/ASME Trans. Mechatron. 27(6), 51245135 (2022).Google Scholar
Boby, R. A., “Kinematic identification of industrial robot using end-effector mounted monocular camera by passing measurement of 3-D pose,” IEEE/ASME Trans. Mechatron. 27(1), 383394 (2022).CrossRefGoogle Scholar
Li, X., Zhang, E., Fang, X. and Zhai, B., “Calibration method for industrial robots based on the principle of perigon error close,” IEEE Access 10, 4856948576 (2022).Google Scholar
Sun, T., B.Lian, S. Y. and Song, Y., “Kinematic calibration of serial and parallel robots based on finite and instantaneous screw theory,” IEEE Trans. Robot. 36(3), 816834 (2020).CrossRefGoogle Scholar
Nguyen, H.-N., Le, P.-N. and Kang, H.-J., “A performance comparison of the full pose and partial pose based robot calibration for various types of robot manipulators,” Adv. Mech. Eng. 13(9), 16878140211047754 (2021).Google Scholar
Wu, J., Liu, Z., Yu, G. and Song, Y., “A study on tracking error based on mechatronics model of a 5-DOF hybrid spray-painting robot,” J. Mech. Sci. Technol. 36(9), 47614773 (2022).CrossRefGoogle Scholar
He, S., Ma, L., Yan, C., Lee, C.-H. and Hu, P., “Multiple location constraints based industrial robot kinematic parameter calibration and accuracy assessment,” Int. J. Adv. Manuf. Technol. 102(5), 10371050 (2019).CrossRefGoogle Scholar
Fu, Z., Pan, J., Spyrakos-Papastavridis, E., Chen, X. and Li, M., “A dual quaternion-based approach for coordinate calibration of dual robots in collaborative motion,” IEEE Robot. Autom. Lett. 5(3), 40864093 (2020).Google Scholar
Xiao, P., Ju, H., Li, Q., Meng, J. and Chen, F., “A new fixed axis-invariant based calibration approach to improve absolute positioning accuracy of manipulators,” IEEE Access 8, 134224134232 (2020).Google Scholar
Gosselin, C. and Angeles, J., “The optimum kinematic design of a planar three-degree-of-freedom parallel manipulator,” J. Mech. Trans. Autom. Des. 110(1), 3541 (1988).Google Scholar
Gosselin, C. and Angeles, J., “A global performance index for the kinematic optimization of robotic manipulators,” J. Mech. Des. 113(3), 220226 (1991).Google Scholar
Patle, B. K., Babu L, G., Pandey, A., Parhi, D. R. K. and Jagadeesh, A., “A review: On path planning strategies for navigation of mobile robot,” Def. Technol. 15(4), 582606 (2019).Google Scholar
Luo, Q., Wang, H., Zheng, Y. and He, J., “Research on path planning of mobile robot based on improved ant colony algorithm,” Neural Comput. Appl. 32(6), 15551566 (2020).CrossRefGoogle Scholar
Jiang, B., Huang, G., Wang, T., Gui, J. and Zhu, X., “Trust based energy efficient data collection with unmanned aerial vehicle in edge network,” Trans. Emerg. Telecommun. Technol. 33(6), e3942 (2022). doi: 10.1002/ett.3942.Google Scholar
Li, Z., Xu, F., Guo, D., Wang, P. and Yuan, B., “New P-type RMPC scheme for redundant robot manipulators in noisy environment,” Robotica 38(5), 775786 (2020).Google Scholar
Rigatos, G., Siano, P. and Raffo, G., “A nonlinear H-infinity control method for multi-DOF robotic manipulators,” Nonlinear Dyn. 88(1), 329348 (2017).Google Scholar