iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://unpaywall.org/10.1007/S10015-019-00552-Y
Neural network-based construction of inverse kinematics model for serial redundant manipulators | Artificial Life and Robotics Skip to main content
Springer Nature Link
Log in

Neural network-based construction of inverse kinematics model for serial redundant manipulators

  • Original Article
  • Published:
Artificial Life and Robotics Aims and scope Submit manuscript

Abstract

Solving the inverse kinematics of redundant manipulators is difficult, because knowledge of the manipulators and their evaluation functions is required. To solve this problem, we propose a novel method of enabling a neural network model to learn the inverse kinematics. The method achieves learning independent of the structure of the evaluation function, by combining multiple neural network models. The method can obtain the neural network models of the inverse kinematics via an automatic calculation process using only training data, which consist of the postures, end-points, and evaluation values. In this paper, the algorithm used by the method and its background is explained, and the effectiveness of the method is validated by a numerical simulation.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Liegeois A (1977) Automatic supervisory control of the configuration and behaviour of multibody mechanisms. IEEE Trans Syst Man Cybern 7(12):868–871

    Article  Google Scholar 

  2. Yoshikawa T (1985) Manipulability and redundancy control of robotic mechanisms. In: Proceedings of 1985 IEEE international conference on robotics and automation, vol 2, pp 1004–1009

  3. Maciejewski AA, Klein CA (1985) Obstacle avoidance for kinematically redundant manipulators in dynamically varying environments. Int J Robot Res 4(3):109–117

    Article  Google Scholar 

  4. Jin L, Li S, La HM, Luo X (2017) Manipulability optimization of redundant manipulators using dynamic neural networks. IEEE Trans Ind Electron 64(6):4710–4720

    Article  Google Scholar 

  5. Xu Z, Li S, Zhou X, Yan W, Cheng T, Huang D (2019) Dynamic neural networks based kinematic control for redundant manipulators with model uncertainties. Neurocomputing 329:255–266

    Article  Google Scholar 

  6. Patel RV, Shadpey. F (2005) Control of redundant robot manipulators. Springer, Berlin

    MATH  Google Scholar 

  7. Bishop CM (2006) Pattern recognition and machine learning (information science and statistics). Springer, Berlin

    MATH  Google Scholar 

  8. Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. CoRR. arxiv:abs/1412.6980

Download references

Author information

Authors and Affiliations

  1. Graduate School of Engineering, University of Hyogo, Himeji, Japan

    Hideaki Takatani, Nozomu Araki, Takao Sato & Yasuo Konishi

Authors
  1. Hideaki Takatani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nozomu Araki
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Takao Sato
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yasuo Konishi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hideaki Takatani.

Additional information

Publisher's Note

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

This work was presented in part at the 24th International Symposium on Artificial Life and Robotics, Beppu, Oita, January 23–25, 2019.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Takatani, H., Araki, N., Sato, T. et al. Neural network-based construction of inverse kinematics model for serial redundant manipulators. Artif Life Robotics 24, 487–493 (2019). https://doi.org/10.1007/s10015-019-00552-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10015-019-00552-y

Keywords

Search

Navigation