Skip to main content
SpringerLink
Log in

Local Path Planning of Mobile Robot Based on Long Short-Term Memory Neural Network

  • Published:
Automatic Control and Computer Sciences Aims and scope Submit manuscript

Abstract

In order to solve the local path planning problem of mobile robot in the unknown environment, more complex control algorithms and solutions are needed for various special situations; however, they cannot be suitable for every environment. A local path planning algorithm of the mobile robot based on Long Short-Term Memory (LSTM) neural network is proposed to simplify the complexity of control, increase the ability of learning and generalization, and improve the efficiency of path planning. Firstly, the model structure of LSTM neural network is designed based on the task requirements of the local path planning of mobile robot. Then, the training data needed by the neural network are collected based on the fuzzy control algorithm and used to train the LSTM model. Finally, the trained model is tested according to the local path planning task. The experimental results show that compared with the fuzzy control algorithm, the designed method improves the calculation velocity, and can adapt to the environment with more obstacles compared with the BP neural network algorithm.

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.
Fig. 9.
Fig. 10.
Fig. 11.
Fig. 12.

Similar content being viewed by others

REFERENCES

  1. Song, X.R., Ren, Y.Y., Gao, S., and Chen, C.B., Survey on technology of mobile robot path planning, Comput. Measur. Control, 2019, vol. 27, no. 4, pp. 1–5, 17.

  2. Yu, Z.Z., Li, Q., and Fan, Q.G., Survey on application of bioinspired intelligent algorithms in path planning optimization of mobile robots, Appl. Res. Comput., 2019, vol. 36, no. 11, pp. 3210–3219.

    Google Scholar 

  3. Cheng, Z., Zhang, Z.A., Li J.Z., and Jiang, T., Mobile robots path planning based on improved artificial potential field, Comput. Eng. Appl., 2019, vol. 55, no. 23, pp. 29–34.

    Google Scholar 

  4. Xie, L.J., Xie, G.R., Chen, H.W., and Li, X.L., Solution to reinforcement learning problems with artificial potential field, J. Central South Univ. Technol., 2008, vol. 15, no. 4, pp. 552–557.

    Article  Google Scholar 

  5. Aouf, A., Boussaid, L., and Sakly, A., Same fuzzy logic controller for two-wheeled mobile robot navigation in strange environments, J. Rob., 2019, vol. 2019.

    Book  Google Scholar 

  6. Gong, M.M., Research on Path Planning Method of Mobile Robot Based on Neural Network, Harbin Inst. Technol., 2017.

    Google Scholar 

  7. Xiong, K.F. and Zhang, H., Path plan for mobile robot under dynamic environment based on the actual membership function, Bull. Sci. Technol., 2015, vol. 31, no. 9, pp. 168–173.

    Google Scholar 

  8. Li, Z.X., Research on intelligent motion strategy of picking robot based on LM-BP neural network, J. Agric. Mech. Res., 2020, vol. 42, no. 7, pp. 224–227, 232.

  9. Su, W.J., Meng, R., and Yu, C.C., A study on soccer robot path planning with fuzzy artificial potential field, IEEE 2010 International Conference on Computing, Control and Industrial Engineering, 2010, vol. 1, pp. 386–390.

  10. Wang, H., Duan, J., Wang, M., Zhao, J., and Dong, Z., Research on robot path planning based on fuzzy neural network algorithm, 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, 2018, pp. 1800–1803.

  11. Ai, R.J.C.T. and Dadios E.P., Neuro-fuzzy mobile robot navigation, 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), 2018.

  12. Nicola, F., Fujimoto, Y., and Oboe, R., A LSTM neural network applied to mobile robots path planning, 2018 IEEE 16th International Conference on Industrial Informatics (INDIN), 2018, pp. 349–354.

  13. Song, X.Y., Liu, Y.T., Xue, L., Wang, J., Zhang, J.Z., Wang, J.Q., Jiang, L., and Cheng, Z.Y., Time-series well performance prediction based on Long Short-Term Memory (LSTM) neural network model, J. Pet. Sci. Eng., 2019, vol. 186, no. 106682.

  14. Sherstinsky, A., Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network, Phys. D: Nonlinear Phenom., 2020, vol. 202, no. 132306.

  15. Dai, S.Z., Li, L., and Li, Z.H., Modeling vehicle interactions via modified LSTM models for trajectory prediction, IEEE Access, 2019, vol. 7, pp. 38287–38296.

    Article  Google Scholar 

  16. Inoue, M., Yamashita, T., and Nishida, T., Robot path planning by LSTM network under changing environment, Adv. Intell. Syst. Comput., 2019, vol. 759, pp. 317–329.

    Google Scholar 

  17. Lee, L., Parisotto, E., Chaplot, D.S., and Salakhutdinov, R., LSTM iteration networks: An exploration of differentiable path finding, International Conference on Learning Representations, 2018.

  18. Yu, Y.J., Cao, J.F., and Zhu, J.Y., An LSTM short-term solar irradiance forecasting under complicated weather conditions, IEEE Access, 2019, vol. 7, pp. 145651–145666.

    Article  Google Scholar 

  19. Ali, M.A.H. and Mailah, M., A simulation and experimental study on wheeled mobile robot path control in road roundabout environment, Int. J. Adv. Rob. Syst., 2019, vol. 16, no. 2.

  20. Wei, L.X., Wu, S.K., Sun, H., and Zheng, J., Mobile robot path planning based on multibehaviours, Control Decis., 2019, vol. 34, no. 12, pp. 2721–2726.

    Google Scholar 

  21. Martinez, F., Jacinto, E., and Montiel, H., Neuronal environmental pattern recognizer: Optical-by-distance LSTM model for recognition of navigation patterns in unknown environments, International Conference on Data Mining and Big Data, Singapore, 2019, vol. 1071, pp. 220–227.

  22. Yu, C.Y., Qi, X., Ma, H.T., Wang, C.R., and Zhao, Y.H., LLR: Learning learning rates by LSTM for training neural networks, Neurocomputing, 2020, vol. 394, pp. 41–50.

    Article  Google Scholar 

  23. Bock, S. and Weis, M., A proof of local convergence for the Adam optimizer, 2019 International Joint Conference on Neural Networks (IJCNN), 2019, pp. 1–8.

Download references

Funding

This work is supported by the National Natural Science Foundation of China grant nos. 61473179, 61973184, 61573213, and SDUT and Zibo City Integration Development of China grant no. 2018ZBXC295.

Author information

Authors and Affiliations

  1. School of Computer Science and Technology Shandong University of Technology, 255049, Zibo, China

    Na Guo, Caihong Li, Di Wang, Guoming Liu & Tengteng Gao

  2. School of Mechanical, Electrical and Information Engineering, Shandong University, 264209, Weihai, China

    Yong Song

Authors
  1. Na Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Caihong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Di Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yong Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Guoming Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Tengteng Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Caihong Li.

Ethics declarations

The authors declare no conflict of interest.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Na Guo, Li, C., Wang, D. et al. Local Path Planning of Mobile Robot Based on Long Short-Term Memory Neural Network. Aut. Control Comp. Sci. 55, 53–65 (2021). https://doi.org/10.3103/S014641162101003X

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S014641162101003X

Keywords:

Search

Navigation