Advertisement

Robot Manipulator Control with Efforts Stabilization in Capture of Object with Fuzzy Geometrical Characteristic

  • V. I. ChizhikovEmail author
  • E. V. Kurnasov
  • A. B. Petrov
Conference paper
Part of the Lecture Notes in Mechanical Engineering book series (LNME)

Abstract

Modern production is becoming “smart” and able to flexibly adapt to customer needs. In high-variety low-volume production, robot manipulators play an important role that has extended functionality for reliable capture of various shape objects, taking into account the uncertain and rapidly changing conditions of the production process. The authors solved the synthesis problem of the regulator control system with efforts stabilization for capturing. The movement of the robot gripper mechanism links has been investigated at the site of the effort development from the smallest value in the object tactile detection case to the maximum permissible condition, which is determined by the possibility of the object catching without the object permanent deformation. The reaction stability in contact of the gripper with the object is ensured by the selection of the numerical values of free parameters of the control law in order to minimize the amplitude of the error signal in the steady-state mode. The control law which implements the controller contains a playback error of the control signal, the first and second derivatives of the output signal. The control object in the control system is a mechanism where the control signal in the form of a gas mass flow rate is fed to controlled elastic kinematic connections. We proposed a control system for stabilizing the gripping effort may be subject to external disturbance.

Keywords

Actuator Controller Control system Robot Robot arm Robot gripper Capture Fuzzy information 

Notes

Acknowledgements

This work was supported by the Russian Foundation for Basic Research (project no. 19-08-00775).

References

  1. 1.
    Zheng P, Wang H, Sang Z, Zhong Ray Y, Liu Y, Liu C, Mubarok K, Yu S, Xu X (2018) Smart manufacturing systems for Industry 4.0: conceptual framework, scenarios, and future perspectives. Front Mech Eng 13(2):137–150.  https://doi.org/10.1007/s11465-018-0499-5
  2. 2.
    Kashirskaya EN, Kurnasov EV, Kholopov VA, Shmeleva AG (2017) Methodology for assessing the implementation of the production process. In: Proceedings of 2017 IEEE 2nd international conference on control in technical systems, CTS 2017, IEEE, pp 232–235.  https://doi.org/10.1109/ctsys.2017.8109533
  3. 3.
    Kashirskaya EN, Kholopov VA, Shmeleva AG, Kurnasov EV (2017) Simulation model for monitoring the execution of technological processes. In: Proceedings of 2017 IEEE 2nd international conference on control in technical systems, CTS 2017. IEEE, pp 307–310.  https://doi.org/10.1109/ctsys.2017.8109553
  4. 4.
    Holopov V, Kushnir A, Kurnasov E, Ganichev A, Romanov A (2017) Development of digital production engineering monitoring system based on equipment state index. In: Proceedings of the 2017 IEEE Russia section young researchers in electrical and electronic engineering conference, ElConRus 2017, IEEE, pp 863–868.  https://doi.org/10.1109/eiconrus.2017.7910692
  5. 5.
    Thoben K-D, Wiesner S, Wuest T (2017) “Industrie 4.0” and smart manufacturing—a review of research issues and application examples. Int J Autom Technol 11(1):4–19.  https://doi.org/10.20965/ijat.2017.p0004
  6. 6.
    Kholopov VA, Kashirskaya EN, Kushnir AP, Kurnasov EV, Ragutkin AV, Pirogov VV (2018) Development of digital machine-building production in the Industry 4.0 concept. J Mach Manuf Reliab 47(4):380–385.  https://doi.org/10.3103/s1052618818040064
  7. 7.
    Bahrin MAK, Othman F, Azli NHN, Talib MF (2016) Industry 4.0: a review on industrial automation and robotic. J Teknol (Sci Eng) 78(6–13):137–143.  https://doi.org/10.11113/jt.v78.9285
  8. 8.
    Hsiao K, Chitta S, Ciocarlie M, Gil Jones E (2010) Contact-reactive grasping of objects with partial shape information. In: 2010 IEEE/RSJ international conference on intelligent robots and systems. Taiwan, pp 1228–1235.  https://doi.org/10.1109/iros.2010.5649494
  9. 9.
    Romano JM, Hsiao K, Niemeyer G, Chitta S, Kuchenbecker KJ (2011) Human-inspired robotic grasp control with tactile sensing. IEEE Trans Rob 27(6):1067–1079.  https://doi.org/10.1109/TRO.2011.2162271CrossRefGoogle Scholar
  10. 10.
    Okur B, Aksoy O, Zergeroglu E, Tatlicioglu E (2015) Nonlinear robust control of tendon-driven robot manipulators. J Intell Rob Syst 80(1):3–14.  https://doi.org/10.1007/s10846-014-0141-7CrossRefGoogle Scholar
  11. 11.
    Droukas L, Doulgeri Z (2016) Rolling contact motion generation and control of robotic fingers. J Intell Rob Syst 82(1):21–38.  https://doi.org/10.1007/s10846-015-0255-6CrossRefGoogle Scholar
  12. 12.
    Gao X, Dawson D, Qu Z (1992) On the robust control of two manipulators holding a rigid object. J Intell Rob Syst 6(1):107–119.  https://doi.org/10.1007/BF00314701CrossRefzbMATHGoogle Scholar
  13. 13.
    Manko SV, Shestakov EI (2018) Automatic synthesis of gait scenarios for reconfigurable mechatronic modular robots in the modification of the walking platform. Ross Tekhnol Zh (Russ Technol J) 6(4):26–41Google Scholar
  14. 14.
    Vorob’ev EI, Dorofeev VO (2017) Orientation mechanism with linear drives for robot manipulators and limb prostheses. Russ Eng Res 37(6):475–478.  https://doi.org/10.3103/s1068798x17060235CrossRefGoogle Scholar
  15. 15.
    Chizhikov VI, Kurnasov EV, Vorob’ev EI (2018) Capture of an object on the basis of tactile surface recognition. Russ Eng Res 38(4):251–255.  https://doi.org/10.3103/s1068798x18040044CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • V. I. Chizhikov
    • 1
    Email author
  • E. V. Kurnasov
    • 1
  • A. B. Petrov
    • 1
  1. 1.MIREA—Russian Technological University (RTU MIREA)MoscowRussia

Personalised recommendations