Skip to main content
SpringerLink
Log in

Multifeature video modularized arm movement algorithm evaluation and simulation

  • S.i.: Ai Based Techniques and Applications for Intelligent Iot Systems
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

With the rapid development of artificial intelligence applications, the practical value of robotic arms is becoming increasingly important. Traditional robotic arms can only grab objects along a preplanned route, and it is difficult to obtain external information. If the surrounding environment is unknown or has changed, the robotic arm needs to be redesigned. Otherwise, grabbing will be difficult. To ensure the coordination ability of the automatic control system of a robotic arm and for the robot to be able to independently recognize the surrounding environment, robotic arm control systems based on multifeature video have gradually become popular. These systems also help to address the problem of independent grasping under unknown conditions. In this study, a multifeature video-based modular robotic arm motion device was built, and the relevant performance of the robotic arm was verified by experiments. The experimental results show that the relative error between the multifeature video vision system and the laser rangefinder is 1.16% at minimum and 3.12% at maximum. The grasping success rate reached 88.9%, and the robotic arm motion device could meet the expected requirements.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

References

  1. Lemak SS, Chertopolokhov VA, Makarov MA (2021) Set of dynamic restrictions imposed on robotic arm-based motion simulator phase coordinates. J Phys: Conf Ser 1864(1):012133–012139

    Google Scholar 

  2. Gao J, Chen Y, Li F (2019) Kinect-based motion recognition tracking robotic arm platform. Intell Control Autom 10(3):79–89

    Article  Google Scholar 

  3. Chen L, Sun H, Zhao W (2021) AI based gravity compensation algorithm and simulation of load end of robotic arm wrist force. Math Probl Eng 2021(8):1–11

    Google Scholar 

  4. Hsieh YZ, Lin SS (2020) Robotic arm assistance system based on simple stereo matching and q-learning optimization. IEEE Sens J 20(18):10945–10954

    Article  Google Scholar 

  5. Pritykin FN, Nebritov VI (2019) Determination of target points approachability by an android robot arm in organized space based on virtual modeling of movements. J Phys: Conf Ser 1260(7):072015–072022

    Google Scholar 

  6. Supriyono S, Widiyanto W, Siswanto WA (2020) Alternative control system for robot arm with data logger. Int J Adv Trends Comput Sci Eng 9(3):3728–3733

    Article  Google Scholar 

  7. Sadiq AT, Raheem FA, Abbas N (2021) Ant colony algorithm improvement for robot arm path planning optimization based on D* strategy. Int J Mech Mechatron Eng 21(1):96–111

    Google Scholar 

  8. Shah H, Kamis Z, Shukor AZ (2019) Optimum utilization of energy consumption in arm robot. Mod Appl Sci 13(5):57–57

    Article  Google Scholar 

  9. Beyhan A, Adar NG (2020) Real-time vision-based grasping randomly placed object by low-cost robotic arm using surf algorithm. IOP Conf Ser: Mater Sci Eng 938(1):012008–012018

    Article  Google Scholar 

  10. Shen QJ, Fahmi M, Annuar K (2018) Efficient object isolation in complex environment using manipulation primitive on a vision based mobile 6DOF robotic arm. Int J Mech Mechatron Eng 18(1):50–56

    Google Scholar 

  11. Wei Y, Jia D (2021) Research on robotic arm movement grasping system based on MYO. J Phys: Conf Ser 1754(1):012173–012179

    Google Scholar 

  12. Tam B, Tao L, Nguyen T (2021) DE-based algorithm for solving the inverse kinematics on a robotic arm manipulators. J Phys: Conf Ser 1922(1):012008–012017

    Google Scholar 

  13. Gavilanes JJ, Pérez M, Villa I (2018) Modeling and simulation of an algorithm for the control of a robotic arm. KnE Eng 1(2):153–153

    Article  Google Scholar 

  14. Adeyeri MK, Ayodeji SP, Olasanoye O (2017) Modelling and simulation of 4 DOF robotic arm for an automated roselle tea processing plant using solidwoks and matlab simulik. IFAC-PapersOnLine 50(2):249–250

    Article  Google Scholar 

  15. Ardila F, Bastidas H, Ceballos C (2017) The configuration space of a robotic arm in a tunnel. SIAM J Discret Math 31(4):2675–2702

    Article  MathSciNet  MATH  Google Scholar 

  16. Bousseta R, Ouakouak IE, Gharbi M (2018) EEG based brain computer interface for controlling a robot arm movement through thought. Innov Res Biomed En 39(2):129–135

    Google Scholar 

  17. Bokhonsky A, Golovin V, Maistrishin M (2020) Optimal translational motion of the elastic telescopic robot arm. IOP Conf Ser: Mater Sci Eng 709(2):022005–022011

    Article  Google Scholar 

Download references

Funding

There is no funding for this study.

Author information

Authors and Affiliations

  1. Department of Sports, Dalian Maritime University, Dalian, 410116, Liaoning, China

    Xiaofang Zhao

Authors
  1. Xiaofang Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaofang Zhao.

Ethics declarations

Conflict of interest

There is no potential conflict of interest in this study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, X. Multifeature video modularized arm movement algorithm evaluation and simulation. Neural Comput & Applic 35, 8637–8646 (2023). https://doi.org/10.1007/s00521-022-08060-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-08060-0

Keywords

Search

Navigation