Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Diagonal recurrent neural networks for parameters identification of terrain based on wheel–soil interaction analysis

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Wheeled mobile robots (WMR) are often applied to travel on outdoor unstructured environment, such as loose soil or variable field terrain. Learning the knowledge of terrain has played a significant role for better mobility and stability of WMR. In this study, a diagonal recurrent neural network-based adaptive method is proposed to identify terrain parameters by the platform of a single driving wheel. According to the classical terramechanics model of wheel–soil interaction, a decoupling simplification model is developed by closed-form analytical equations. Five unknown terrain parameters are divided into two groups and included in two complex nonlinear equations. These parameters are used to compute the model outputs of force and torque of wheel–soil interaction. Dynamic back propagation algorithm is applied to update these parameters for compensating the errors between the prediction of neural network and measurable data in real time. The results of simulation show that the terrain parameters can be obtained and approximate the experimental value of terrain parameters when the predictive errors converge to zero.

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

Similar content being viewed by others

Explore related subjects

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

References

  1. Wong JY (2001) Theory of ground vehicles. Wiley, USA

    Google Scholar 

  2. Iagnemma K, Kang S, Shibly H, Dubowsky S (2004) Online terrain parameter estimation for wheeled mobile robots with application to planetary rovers. IEEE Trans Robot 20(5):921–927

    Article  Google Scholar 

  3. Iagnemma K, Senatore C, Trease B et al (2011) Terramechanics modeling of Mars surface exploration rovers for simulation and parameter estimation. In: Proceedings of the ASME international design engineering technical conference and computers and information in engineering conference, Washington, DC

  4. Moreland S, Skonieczny K, Wettergreen D (2012) Motion analysis system for robot traction device evaluation and design. In: Proceedings of the international conference on field and service robotics, Sendai, Japan

  5. Ding L, Deng Z, Gao H et al (2014) Interaction mechanics model for rigid driving wheels of planetary rovers moving on sandy terrain with consideration of multiple physical effects. J Field Robot. doi:10.1002/rob.21533

    Google Scholar 

  6. Zhang G, Eddy PB, Michael HY (1998) Forecasting with artificial neural networks. The state of the art. Int J Forecast 14(1):35–62

    Article  Google Scholar 

  7. Huang GB, Zhu Q, Siew C (2006) Real-time learning capability of neural networks. IEEE Trans Neural Netw 17(4):863–878

    Article  Google Scholar 

  8. Yang CG, Li ZJ, Cui R, Xu B (2014) Neural network-based motion control of an underactuated wheeled inverted pendulum model. IEEE Trans Neural Netw Learn Syst 25(11):2004–2016

    Article  Google Scholar 

  9. Cao F, Lin S, Xu Z (2010) Approximation capability of interpolation neural networks. Neurocomputing 74(1):457–460

    Article  Google Scholar 

  10. Yang DS, Liu XR, Xu YK, Wang YC, Liu ZB (2013) State estimation of recurrent neural networks with interval time-varying delay: an improved delay-dependent approach. Neural Comput Appl 23(3–4):1149–1158

    Google Scholar 

  11. Yu DL, Chang TK (2005) Adaptation of diagonal recurrent neural network model. Neural Comput Appl 14(3):189–197

    Article  Google Scholar 

  12. Ray LR, Brande DC, Lever JH (2009) Estimation of net traction for differential-steered wheeled robots. J Terrramech 46:75–87

    Article  Google Scholar 

  13. Ding L, Yoshida K, Nagatani K, Gao HB, Deng ZQ (2009) Parameter identification for planetary soil based on decoupled analytical wheel–soil interaction terramechanics model. In: IEEE/RSJ international conference on intelligent robots and systems, pp 4122–4127

  14. Ding L, Gao HB, Deng ZQ et al (2011) Experimental study and analysis on driving wheels’ performance for planetary exploration rovers moving in deformable soil. J Terramech 48:27–45

    Article  Google Scholar 

  15. Li ZJ, Yang CG (2012) Neural-adaptive output feedback control of a class of transportation vehicles based on wheeled inverted pendulum models. IEEE Trans Control Syst Technol 20:1583–1591

    Article  Google Scholar 

  16. Jung JR, Yum BJ (2011) Artificial neural network based approach for dynamic parameter design. Expert Syst Appl 38:504–510

    Article  Google Scholar 

  17. Haykin SS (2009) Neural networks and learning machines, 3rd edn. Pearson Education, Upper Saddle River

    Google Scholar 

  18. Zhang R, Xu Z, Huang G, Wang D (2012) Global convergence of online BP training with dynamic learning rate. IEEE Trans Neural Netw Learn Syst 23(2):330–341

    Article  Google Scholar 

  19. Modares H, Lewis FL, Naghibi-Sistani M (2013) Adaptive optimal control of unknown constrained-input systems using policy iteration and neural networks. IEEE Trans Neural Netw Learn Syst. 24(10):1513–1525

    Article  Google Scholar 

  20. Mastorocostas PA, Hilas CS (2009) A block-diagonal recurrent fuzzy neural network for system identification. Neural Comput Appl 18(7):707–717

    Article  Google Scholar 

Download references

Acknowledgments

This study was supported in part by the National Natural Science Foundation of China (Grant No. 61370033/51275106), National Basic Research Program of China (Grant No. 2013CB035502), Fundamental Research Funds for the Central Universities (Grant No. HIT.BRETIII.201411), Self-Planned Task (No. SKLRS201401A01/SKLRS201501B) of State Key Laboratory of Robotics and System (HIT), Harbin Talent Programme for Distinguished Young Scholars (No. 2014RFYXJ001); and the “111 Project” (Grant No. B07018).

Author information

Authors and Affiliations

  1. State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, 150001, China

    Xingguo Song, Haibo Gao, Liang Ding, Zongquan Deng & Chen Chao

  2. Mechanical Engineering School, Southwest Jiaotong University, Chengdu, 610031, China

    Xingguo Song

Authors
  1. Xingguo Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Haibo Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Liang Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zongquan Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chen Chao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Xingguo Song or Haibo Gao.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Song, X., Gao, H., Ding, L. et al. Diagonal recurrent neural networks for parameters identification of terrain based on wheel–soil interaction analysis. Neural Comput & Applic 28, 797–804 (2017). https://doi.org/10.1007/s00521-015-2107-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-015-2107-5

Keywords

Search

Navigation