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Experimental Evaluation of Microrobot Positioning Accuracy

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Abstract

This article discusses the potential applications of robotic micropositioning systems in applications ranging from industry to research. The demand for micropositioning systems is increasing due to the rapid development of the technology and its wide range of applications. This paper describes a general-purpose microobject manipulation platform consisting of a microtool control system and a microobject detection/recognition system. The development of the manipulation tool is discussed, and experimental studies on micropositioning are carried out. The workflow, accuracy, repeatability, and resonant frequencies of the system are the most important precision characteristics required for the design of the manipulation system under development. The paper concludes with the results of the investigations and the technical characteristics of the microobject manipulation platform.

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REFERENCES

  1. Juuti, J., Leinonen, M., and Jantunen, H., Micropositioning, Piezoelectric and Acoustic Materials for Transducer Applications, Safari, A. and Akdoğan, E.K., Eds., Boston: Springer, 2008, pp. 319–340. https://doi.org/10.1007/978-0-387-76540-2_16

    Book  Google Scholar 

  2. He, S., Tang, H., Qiu, Q., Xiang, X., Che, J., Chen, Ch., Gao, J., Chen, X., and He, Yu., A novel flexure-based XYθ motion compensator: Towards high-precision wafer-level chip detection, IEEE 18th Electronics Packaging Technology Conf. (EPTC), Singapore, 2016, IEEE, 2016, pp. 381–387. https://doi.org/10.1109/EPTC.2016.7861509

  3. Tayouri, S., Izadi, I., and Ghaisari, J., Modeling and parameter identification of piezoelectric actuator in micropositioning systems, 27th Iranian Conf. on Electrical Engineering (ICEE), Yazd, Iran, 2019, IEEE, 2019, pp. 1193–1198. https://doi.org/10.1109/IranianCEE.2019.8786431

  4. Poletkin, K.V., Static pull-in behavior of hybrid levitation micro-actuators: Simulation, modelling and experimental study, IEEE/ASME Trans. Mechatronics, 2020, vol. 26, no. 2, pp. 753–764. https://doi.org/10.1109/TMECH.2020.2999516

    Article  Google Scholar 

  5. Nguyen, X.-H., Mau, T.-H., Meyer, I., Dang, B.-L., and Pham, H.-Ph., Improvements of piezo-actuated stick-slip micro-drives: Modeling and driving waveform, Coatings, 2018, vol. 8, no. 2, p. 62. https://doi.org/10.3390/coatings8020062

    Article  Google Scholar 

  6. Najar, F., Choura, S., El-Borgi, S., Abdel-Rahman, E.M., and Nayfeh, A., Modeling and design of variable-geometry electrostatic microactuators, J. Micromech. Microengineering, 2005, vol. 15, no. 3, pp. 419–429. https://doi.org/10.1088/0960-1317/15/3/001

    Article  MATH  Google Scholar 

  7. Subačiūtė-Žemaitienė, J., Trečiokaitė, V., Šešok, N., Šutinys, E., Dzedzickis, A., Tamošiūnas, Ju., Masalskyi, V., and Bučinskas, V., Mathematical modelling and theoretical research of micropositioning system, IOP Conf. Ser.: Mater. Sci. Eng, 2022, vol. 1239, p. 12011. https://doi.org/10.1088/1757-899x/1239/1/012011

  8. Bejar, E. and Moran, A., Deep reinforcement learning based neuro-control for a two-dimensional magnetic positioning system, 4th Int. Conf. on Control, Automation and Robotics (ICCAR), Auckland, New Zealand, 2018, IEEE, 2018, pp. 268–273. https://doi.org/10.1109/ICCAR.2018.8384682

  9. Zhou, M., Yu, Ye., Zhang, J., and Gao, W., Iterative learning and fractional order PID hybrid control for a piezoelectric micro-positioning platform, IEEE Access, 2020, vol. 8, pp. 144654–144664. https://doi.org/10.1109/ACCESS.2020.3014725

    Article  Google Scholar 

  10. Xu, J., Qin, K., Xu, Y., and Ji, W., Method combining machine vision and machine learning for reed positioning in automatic aerophone manufacturing, 4th Int. Conf. on Robotics and Automation Engineering (ICRAE), Singapore, 2019, IEEE, 2019, pp. 140–147. https://doi.org/10.1109/ICRAE48301.2019.9043784

  11. Leroux, M., Raison, M., Adadja, T., and Achiche, S., Combination of eyetracking and computer vision for robotics control, 2015 IEEE Conf. Technol. Pract. Robot Appl. (TePRA), 2015, pp. 1–6. https://doi.org/10.1109/TePRA.2015.7219692

  12. Bucinskas, V., Dzedzickis, A., Sumanas, M., Sutinys, E., Petkevicius, S., Butkiene, J., Virzonis, D., and Morkvenaite-Vilkonciene, I., Improving industrial robot positioning accuracy to the microscale using machine learning method, Machines, 2022, vol. 10, no. 10, p. 940. https://doi.org/10.3390/machines10100940

    Article  Google Scholar 

  13. Sumanas, M., Petronis, A., Bucinskas, V., Dzedzickis, A., Virzonis, D., and Morkvenaite-Vilkonciene, I., Deep Q-learning in robotics: Improvement of accuracy and repeatability, Sensors, 2022, vol. 22, no. 10, p. 3911. https://doi.org/10.3390/s22103911

    Article  Google Scholar 

  14. Jordan, M.I. and Mitchell, T.M., Machine learning: Trends, perspectives, and prospects, Science, 2015, vol. 349, no. 6245, pp. 255–260. https://doi.org/10.1126/science.aaa8415

    Article  MathSciNet  MATH  Google Scholar 

  15. Rahimi, H.N. and Nazemizadeh, M., Dynamic analysis and intelligent control techniques for flexible manipulators: A review, Adv. Rob., 2014, vol. 28, no. 2, pp. 63–76. https://doi.org/10.1080/01691864.2013.839079

    Article  Google Scholar 

  16. Sabarianand, D.V., Karthikeyan, P., and Muthuramalingam, T., A review on control strategies for compensation of hysteresis and creep on piezoelectric actuators based micro systems, Mech. Syst. Signal Process., 2020, vol. 140, p. 106634. https://doi.org/10.1016/j.ymssp.2020.106634

    Article  Google Scholar 

  17. Bučinskas, V., Subačiūtė-Žemaitienė, J., Dzedzickis, A., Šutinys, E., and Morkvėnaitė-Vilkončienė, I., Robotic micromanipulation: b) Grippers for biological objects, Robotic Syst. Appl., 2022, vol. 2, no. 1, pp. 1–14. https://doi.org/10.21595/rsa.2022.22324

    Article  Google Scholar 

  18. Juhász, L., Maas, J., and Borovac, B., Parameter identification and hysteresis compensation of embedded piezoelectric stack actuators, Mechatronics, 2011, vol. 21, no. 1, pp. 329–338. https://doi.org/10.1016/j.mechatronics.2010.12.006

    Article  Google Scholar 

  19. Xu, Zh.-Zh., Liu, X.-J., and Lyu, S.-K., Study on positioning accuracy of nut/shaft air cooling ball screw for high-precision feed drive, Int. J. Precis. Eng. Manuf., 2014, vol. 15, pp. 111–116. https://doi.org/10.1007/s12541-013-0312-7

    Article  Google Scholar 

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Funding

This research was supported by the Lithuania-Latvia-China (Taiwan) program project “Development and application of a microrobot based on image recognition and machine learning to study single living cells” project no. 01.2.2-LMT-K-718-03-0063.

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Correspondence to Jurga Subačiūtė-Žemaitienė.

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Jurga Subačiūtė-Žemaitienė, Dzedzickis, A., Bučinskas, V. et al. Experimental Evaluation of Microrobot Positioning Accuracy. Aut. Control Comp. Sci. 57, 439–448 (2023). https://doi.org/10.3103/S0146411623050103

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