Preview

Mekhatronika, Avtomatizatsiya, Upravlenie

Advanced search
Open Access Open Access  Restricted Access Subscription or Fee Access

Method for Torsional Stiffness Coefficient Identification of a Manipulator Rotational Joint using an Inertial Measurement Unit

https://doi.org/10.17587/mau.26.594-604

Abstract

Knowledge of the manipulator’s joints and links stiffness parameters is often necessary for high-precision control in the presence of external loads. The article proposes a method for identifying torsion stiffness coefficient of a rotational joint of a 1-DOF manipulator using a microelectromechanical inertial measurement module (IMU) consisting of an accelerometer and an angular velocity sensor. The method is based on describing the dynamics of a joint with an unknown torsional stiffness coefficient using a transfer function (TF) with known input and output signals (the angle of rotation of the motor and the angle of rotation of the link, respectively). The input signal of the TF is read from the engine encoder, and the output signal is reconstructed from the data from the IMU. When harmonic signals with different frequencies are assigned to the drive, the amplitude change and phase shift of the signal passing through the system are measured. Based on the data obtained, the amplitude-phase frequency response of the system is constructed, from which the time constants of the TF describing this system are calculated. The time constants depend on the mechanical characteristics of the system, including the torsion stiffness coefficient of the virtual spring, which is calculated based on the identified time constants. The method, unlike the existing ones, does not require bulky equipment for applying external forces to the manipulator, nor expensive measuring systems for measuring displacements in space. The results of experimental validation on a 1-DOF manipulator confirm the workability of the proposed method.

About the Authors

D. A. Yukhimets
Institute of Marine Technology Problems FEB RAS; Institute of Automation and Control Processes FEB RAS
Russian Federation

Dr. Sci. Tech., Head of Research,

Vladivostok, 690041.



I. M. Grigorev
Institute of Marine Technology Problems FEB RAS; Institute of Automation and Control Processes FEB RAS
Russian Federation

Vladivostok, 690041.



References

1. Wang C., Zheng M., Wang Z., Tomizuka M. Robust TwoDegree-of-Freedom Iterative Learning Control for Flexibility Compensation of Industrial Robot Manipulators // IEEE International Conference on Robotics and Automation (ICRA). 2016. P. 2381—2386.

2. Klimchik A., Furet B., Caro S., Pashkevich А. Identification of the manipulator stiffness model parameters in industrial environment // Mechanism and Machine Theory. 2015. Vol. 90. P. 1—22. DOI: 10.1016/j.mechmachtheory.2015.03.002.

3. Mikhel S. K., Klimchik А. S. Stiffness Model Reduction for Manipulators with Double Encoders: Algebraic Approach // Rus. J. Nonlin. Dyn. 2021. Vol. 17, N. 3. P. 347—360.

4. Ilyukhin Yu. V., Kolesnichenko R. V. Accuracy of Milling by Robots with Two-Motor Servo Drives // Russian Engineering Research. 2019.. Vol. 39, N. 12. P. 1069—1072.

5. Klimchik A., Pashkevich A., Chablat D. CAD-based approach for identification of elasto-static parameters of robotic manipulators // Finite Elements in Analysis and Design. 2013. Vol. 75. P. 19—30.

6. Hu M., Wang H., Pan X. et al. Elastic deformation modeling of series robots with consideration of gravity // Intel Serv Robotics. 2022. Vol. 15. P. 351—362. DOI: 10.1007/s11370-022-00426-6.

7. Monsarrat B. et al. In-situ elastic calibration of robots: Minimally-invasive technology, cover-based pose search and aerospace case studies // Robotics and Computer-Integrated Manufacturing. 2024. Vol. 89.

8. Yukhimets D., Gubankov А. Method of identification of kinematic and elastostatic parameters of multilink manipulators without external measuring devices // IFAC-PapersOnLine. 2020. Vol. 53, Iss. 2. P. 9879—9884.

9. Zollo L., Lopez E., Spedaliere L., Aracil N. G., Guglielmelli E. Identification of Dynamic Parameters for Robots with Elastic Joints // Advances in Mechanical Engineering. 2014. Id 843186.

10. Zhao P. А novel parameter identification method for flexible-joint robots using input torque and motor-side motion data // Robotica. 2022. Vol. 40. P. 1—11. DOI: 10.1017/S0263574722000066.

11. Miranda-Colorado R., Moreno-Valenzuela J. Experimental parameter identification of flexible joint robot manipulators // Robotica. 2017. Vol. 36, Iss. 03. P. 313—332. DOI: 10.1017/s0263574717000224.

12. Tsang K., Chan W. Non-destructive stiffness detection of utility wooden poles using wireless MEMS sensor // Measurement. 2011. Vol. 44. P. 1201—1207. DOI: 10.1016/j.measurement.2011.03.025.

13. Kustiana W., Trilaksono B., Riyansyah M. et al. Bridge Damage Detection with Support Vector Machine in Accelerometer-Based Wireless Sensor Network // J. Vib. Eng. Technol. 2024. Vol. 12. P. 21—40. DOI: 10.1007/s42417-024-01400-5.

14. Pham M., Gautier M., Poignet P. Accelerometer Based Identification of Mechanical Systems // IEEE International Conference on Robotics and Automation. 2002. Vol. 4. P. 4293—4298. DOI: 10.1109/ROBOT.2002.1014433.

15. Nedelchev S., Kirsanov D., Gaponov I. IMU-based Parameter Identification and Position Estimation in Twisted String Actuators // 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). DOI: 10.1109/iros45743.20.

16. Karimov A., Kopets E., Nepomuceno E., Butusov D. Integrate-and-Differentiate Approach to Nonlinear System Identification // Mathematics. 2021. Vol. 9, Iss. 23. DOI: 10.3390/math9232999.

17. Kopets E., Karimov A., Scalera L., Butusov D. Estimating Natural Frequencies of Cartesian 3D Printer Based on Kinematic Scheme // Applied Sciences. 2022. Vol. 12. P. 4514. DOI: 10.3390/app12094514.

18. Zhu W.-H., Lamarche T. Velocity Estimation by Using Position and Acceleration Sensors // IEEE Transactions on Industrial Electronics. 2007. Vol. 54, Iss. 5. P. 2706—2715.

19. Roan P., Deshpande N., Wang Y., Pitzer В. Manipulator state estimation with low cost accelerometers and gyroscopes // IEEE/RSJ International Conference on Intelligent Robots and Systems. Vilamoura-Algarve, Portugal, 2012. P. 4822—4827. DOI: 10.1109/IROS.2012.6385893.

20. Chen H., Ahmadi K. Estimating pose-dependent FRF in machining robots using multibody dynamics and Gaussian Process Regression // Robotics and Computer-Integrated Manufacturing. 2022. Vol. 77. DOI: 10.1016/j.rcim.2022.102354.

21. Targ S. M. А short course in theoretical mechanics: Textbook for universities, Moscow, Higher school, 2010, 323 p. (in Russian)

22. Smith J. O. Mathematics of the Discrete Fourier Transform (DFT). Stanford: CCRMA, 2002.


Review

For citations:


Yukhimets D.A., Grigorev I.M. Method for Torsional Stiffness Coefficient Identification of a Manipulator Rotational Joint using an Inertial Measurement Unit. Mekhatronika, Avtomatizatsiya, Upravlenie. 2025;26(11):594-604. (In Russ.) https://doi.org/10.17587/mau.26.594-604

Views: 13


ISSN 1684-6427 (Print)
ISSN 2619-1253 (Online)