Hostname: page-component-8448b6f56d-jr42d Total loading time: 0 Render date: 2024-04-19T23:29:59.971Z Has data issue: false hasContentIssue false
  • English
  • Français

An attitude measurement method of industrial robots based on the inertial technology

Published online by Cambridge University Press:  06 April 2022

Rui Li ,
Xiaoling Cui Open the ORCID record for Xiaoling Cui [Opens in a new window] ,
Jiachun Lin  and
Yanhong Zheng
Rui Li
Affiliation:
Beijing Engineering Research Center of Precision Measurement Technology and Instruments, Beijing University of Technology, Beijing, China
Xiaoling Cui
Affiliation:
Beijing Engineering Research Center of Precision Measurement Technology and Instruments, Beijing University of Technology, Beijing, China
Jiachun Lin*
Affiliation:
Beijing Engineering Research Center of Precision Measurement Technology and Instruments, Beijing University of Technology, Beijing, China
Yanhong Zheng
Affiliation:
Beijing Institute of Spacecraft System Engineering, Beijing, China
*
*Corresponding Author. E-mail: linjc@bjut.edu.cn
Get access Rights & Permissions [Opens in a new window]

Abstract

The attitude control error of the robot end-effector directly affects the manufacturing accuracy. The study aims to develop a real-time measurement method of the industrial robot end-effector attitude in the field environment for improving the control accuracy of robot attitude.

In this paper, an attitude measurement method of robot end-effector based on the inertial technology was proposed. First, an inertial measurement system was designed, and the measurement parameters and installation errors were calibrated. Then the inertia measurement principle of robot end-effector attitude was explored, and the robot end-effector attitude measurement was realized with the fourth-order Runge−Kutta algorithm. In addition, the influence of the data processing algorithm and sampling frequency on the attitude accuracy was analyzed. Finally, a test platform was built to experimentally explore the proposed inertial measurement method.

The inertial measured data were compared with the data obtained with the laser tracker. The measurement accuracy of the inertial measurement method reached 0.15°, which met the accuracy requirements of real-time measurements of robot end-effector attitude in the manufacturing field.

The method proposed in this paper is convenient and can realize the real-time attitude measurement of industrial robot. The measurement results can compensate the attitude control error of the robot end-effector and improve the attitude control accuracy of the robot.

Keywords

industrial robotinertial technologyattitude measurementerror compensationfourth-order Runge−Kutta algorithm
Type
Research Article
Information
Robotica , Volume 40 , Issue 8 , August 2022 , pp. 2909 - 2931
Copyright
© The Author(s), 2022. 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

Zhang, J., Zhang, B., Zhang, N., Wang, C. and Chen, Y., “A novel reliable robust adaptive event-triggered automatic steering control approach of autonomous land vehicles under communication delay,” Int. J. Robust Nonlinear Control 31(7), 2436–2464 (2021).CrossRefGoogle Scholar
Cao, X., Hu, Y. and Wang, T., “Research on attitude solution of IMU based on ROS and Quaternion complementary filtering,” Ind. Control Comput. 30(11), 63–64 (2017).Google Scholar
Qu, X. and Zhang, X., “Research on online error measurement and real-time control compensation technology for industrial robots,” J. Mech. Eng. 53(8), 1–7 (2017).Google Scholar
Ambarish, G., Atthur, Q. and Michael, P., “Identifying Robot Parameters Using Partial Pose Information,” In: IEEE International Conference on Systems, Man and Cybernetics , Chicago (1992) pp. 18–21.Google Scholar
Park, K. T., Park, C. H. and Shin, Y. J., “Performance Test of Industrial Dual Arm Robot,” In: IEEE International Conference on Industrial Informatics , (IEEE (2008) pp. 425–429.Google Scholar
Chen, S., Niu, J. L. and Yang, R. J., “Design of the position computing for self-propelled underground tunneling robots,” Chin. J. Sens. Actuators 26(6), 838–844 (2013).Google Scholar
Han, W.. Research on the 6-Dof Position and Orientation Measurement System (Xidian University, 2018).Google Scholar
Li, X. and Huang, S., “Research on robot motion attitude measurement based on laser technology,” Laser J. 40(3), 192–195 (2019).Google Scholar
Sato, O., Shimojima, K., Olea, G., Furutani, R. and Takamasu, K., “Full Parameter Calibration of Parallel Mechanism,” In: Procof 4th euspen International Conference , Glasgow, Scotland, UK (2004) pp. 1–2.Google Scholar
Cechowicz, R., “Indoor Mobile Robot Attitude Estimation with MEMS Gyroscope,” In: 2nd International Conference of Computational Methods in Engineering Science (CMES) , Lublin, Poland (2017).Google Scholar
Liu, Y., Li, Y., Zhuang, Z., Song, T., “Improvement of robot accuracy with an optical tracking system,” Sensors 20(21), 6341 (2020).CrossRefGoogle ScholarPubMed
Gharaaty, S., Shu, T., Joubair, A., Xie, W. F. and Bonev, I. A., “Online pose correction of an industrial robot using an optical coordinate measure machine system,” Int. J. Adv. Robot. Syst. 15(4), 172988141878791 (2018).CrossRefGoogle Scholar
Jeon, S., Tomizuka, M. and Katou, T., “Kinematic kalman filter (KKF) for robot end-effector sensing,” J. Dyn. Syst. Meas. Control 131(2), 211–216 (2009).CrossRefGoogle Scholar
Mansoor, S., Bhatti, U. I., Bhatti, A. I. and Ali, S. M. D., “Improved attitude determination by compensation of gyroscopic drift byuse of accelerometers and magnetometers,” Meas. J. Int. Meas. Confed., 131, 582–589 (2019).CrossRefGoogle Scholar
Roan, P., Deshpande, N., Wang, Y. and Pitzer, B., “Manipulator state estimation with low cost accelerometers and gyroscopes,”In: IEEE Int. Conf. Intell. Robots Syst. (2012) pp. 4822–4827.Google Scholar
Simon, R., Schmidt, M. A. and Courville, N., “Test and Evaluation of an Air Force Non-Developmental Item (NDI) Computer System,” In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting , SAGE Publications (1988) pp. 1162–1165.Google Scholar
Glowka, L., “Bioprospecting, alien invasive species, and hydrothermal vents: Three emerging legal issues in the conservation and sustainable use of biodiversity,” Tul. Envtl. LJ, 13 (2), 329–360 (2000).Google Scholar
Slamani, M., Nubiola, A. and Bonev, I., “Assessment of the positioning performance of an industrial robot,” Ind. Robot. 39(1), 57–68 (2012).CrossRefGoogle Scholar
Xiaorong, C., Ping, C. and Wenkang, S., “Dyn amic measuring the position of a moving object based on laser tracking system,” Chin. J. Sci. Instrum. 25 (6), 777–780+819 (2004).Google Scholar
Chiella, A. C. B., Teixeira, B. O. S. and Pereira, G. A. S., “Quaternion-based robust attitude estimation using an adaptive unscented kalman filter,” Sensors 19(10), 2372 (2019).CrossRefGoogle ScholarPubMed
Ban, C., Ren, G. and Chen, X., “Research on IMU based robot attitude adaptive EKF measurement algorithm,” Chin. J. Sci. Instrum. 41(2), 33–39 (2020).Google Scholar
Wang, L., Zhai, K., He, W. and Xu, J. H., “Application of fourth-order Runge-Kutta algorithm in SINS,” Comput. Simul. 31(11), 56–59 (2014).Google Scholar
Shi, K. and Liu, M., “Strapdown inertial navigation quaternion fourth-order Runge-Kutta attitude algorithm,” J. Detect. Control 41(3), 61–65 (2019).Google Scholar
Liang, S., Xu, X. S. and Huang, Y. L., “Application of Sage-Husa adaptive filter to integrated navigation system,” J. Test Meas. Technol. 25(4), 327–331 (2011).Google Scholar
Xiaojun, Z., Zihan, X. and Shipeng, Y., “Research on solving algorithm of robot attitude,” Mach. Design Manufact. 6, 246–249 (2018).Google Scholar
Luqiang, S., Yigang, H., Qiwu, L. and Wei, H., “Research on the measurement method of tilt angle based on sensor data fusion,” Chin. J. Sci. Instrum. 38(7), 1683–1689 (2017).Google Scholar
Chen, D.. Research on Attitude Measurement and Parameter Calculation of a Product (North University of China, 2016).Google Scholar
Qi, H., Zhang, B., Zhang, N., Zheng, M. and Chen, Y., “Enhanced lateral and roll stability study for a two-axle bus via hydraulically interconnected suspension tuning,” SAE Int. J. Veh. Dyn. Stab. NVH 3(1), 5–18 (2019).CrossRefGoogle Scholar
Qi, H., Chen, Y., Zhang, N., Zhang, B., Wang, D., Tan, B., “Improvement of both handling stability and ride comfort of a vehicle via coupled hydraulically interconnected suspension and electronic controlled air spring,” Proc. Inst. Mech. Eng. D J. Automob. Eng. 234, 2–3 (2020).CrossRefGoogle Scholar
Chen, Y., Mendoza, A. S. E. and Griffith, D. T., “Experimental and numerical study of high-order complex curvature mode shape and mode coupling on a three-bladed wind turbine assembly,” Mech. Syst. Signal Process. 160(3), 10787 (2021).CrossRefGoogle Scholar
Chen, Y., Joffre, D. and Avitabile, P., “Underwater dynamic response at limited points expanded to full-field strain response,” J. Vib. Acoust. 140(5), 051016 (2018).CrossRefGoogle Scholar