Skip to main content
SpringerLink
Log in

Design and Implementation of the Trajectory Tracking and Dynamic Obstacle Avoidance of Wheeled Mobile Robot Based on T–S Fuzzy Model

  • Published:
International Journal of Fuzzy Systems Aims and scope Submit manuscript

Abstract

In this paper, an intelligent nonholonomic wheeled mobile robot (WMR) for real-time trajectory tracking and dynamic obstacle avoidance is developed. The tracking controller of the WMR is designed based on the Takagi–Sugeno (T–S) fuzzy model. A handheld device adopted as the tracking object of the WMR is developed to measure the distance between the WMR and tracking object. The linear and angular velocities of the tracking object are incorporated into the dynamic model of the WMR; subsequently, the state equation of the attitude error of the WMR can be obtained. For obstacle avoidance, the deep image algorithm is utilized to estimate the distance between the WMR and obstacle. Next, the fixed-point tracking algorithm is used for object avoidance in indoor and outdoor environments. The simulation results reveal that the attitude error between the WMR and tracking object for dynamic tracking can converge to zero after a few seconds. Experimental results are provided to demonstrate the effectiveness and feasibility of the designed WMR and handheld device.

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
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23

Similar content being viewed by others

Data Availability

The data are available upon request from the authors

References

  1. Rotondo, D., Puig, V., Nejjari, F., Romera, J.: A fault-hiding approach for the switching Quasi-LPV fault-tolerant control of a four-wheeled omnidirectional mobile robot. IEEE Trans. Ind. Electron. 62(6), 3932–3944 (2014)

    Google Scholar 

  2. Kim, C., Suh, J., Han, J.: Development of a hybrid path planning algorithm and bio-inspired control for an omni-wheel mobile robot. Sensors 20, 4258 (2020). https://doi.org/10.3390/s20154258

    Article  Google Scholar 

  3. Tsai, S.-H., Kao, L.-H., Lin, H.-Y., Lin, T.-C., Song, Y.-L., Chang, L.-M.: A sensor fusion based nonholonomic wheeled mobile robot for tracking control. Sensors 20(24), 7055 (2020). https://doi.org/10.3390/s20247055

    Article  Google Scholar 

  4. Li, I.-H., Chien, Y.-H., Wang, W.-Y., Kao, Y.-F.: Hybrid intelligent algorithm for indoor path planning and trajectory-tracking control of wheeled mobile robot. Int. J. Fuzzy Syst. 18, 595–608 (2016)

    Article  MathSciNet  Google Scholar 

  5. Chen, W.-J., Jhong, B.-G., Chen, M.-Y.: Design of path planning and obstacle avoidance for a wheeled mobile robot. Int. J. Fuzzy Syst. 18, 1080–1091 (2016)

    Article  MathSciNet  Google Scholar 

  6. Dian, S., Han, J., Guo, R., Li, S., Zhao, T., Hu, Y., Wu, Q.: Double closed-loop general type-2 fuzzy sliding model control for trajectory tracking of wheeled mobile robots. Int. J. Fuzzy Syst. 21, 2032–2042 (2019)

    Article  MathSciNet  Google Scholar 

  7. Lu, Q., Zhang, D., Ye, W., Fan, J., Liu, S., Su, C.-Y.: Targeting posture control with dynamic obstacle avoidance of constrained uncertain wheeled mobile robots including unknown skidding and slipping. IEEE Trans. Syst. Man, Cybern.: Syst. 51(11), 6650–6659 (2021)

    Article  Google Scholar 

  8. Gao, X., Gao, R., Liang, P., Zhang, Q., Deng, R., Zhu, W.: A hybrid tracking control strategy for nonholonomic wheeled mobile robot incorporating deep reinforcement learning approach. IEEE Access 9, 15592–15602 (2021)

    Article  Google Scholar 

  9. Takagi, T., Sugeno, M.: Fuzzy identification of system and its applications to modeling and control. IEEE Trans. Syst. Man Cybern 15(1), 116–132 (1985)

    Article  MATH  Google Scholar 

  10. Zhang, T., Xu, X.: A new method of seamless land navigation for GPS/INS integrated system. Measurement 45(4), 691–701 (2012)

    Article  Google Scholar 

  11. G.-R. Yu and K.-Y. Pai, 2011 “Path tracking for a wheel mobile robot using T-S fuzzy control,” In 8th Asian Control Conference (ASCC), Kaohsiung, pp. 424–429.

  12. Intel, “Intel RealSense depth camera D435”, [Online]. Available: https://www.intelrealsense.com/depth-camera-d435/

  13. Tanaka, K., Wang, H.O.: Fuzzy Control Systems Design and Analysis: A Linear Matrix Inequality Approach. Wiley, New York (2001)

    Book  Google Scholar 

  14. Lee, J.B., Park and G. Chen,: Robust fuzzy control of nonlinear system with parametric uncertainties. IEEE Trans. Fuzzy Syst. 9(2), 369–379 (2001)

    Article  Google Scholar 

  15. Xie, L.: Output feedback H∞ control of systems with parameter uncertainty. Int. J. Control 63(4), 741–750 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  16. Tsai, S.-H., Wang, J.-W., Song, E.-S., Lam, H.-K.: Robust control for nonlinear hyperbolic PDE systems based on the polynomial fuzzy model. IEEE Trans. on Cybernetics 51(7), 3789–3801 (2021)

    Article  Google Scholar 

  17. Sanhoury, I.M.H., Amin, S.H.M., Husain, A.R.: Tracking control of nonholonomic wheeled mobile robot. Precis. Instrum. Mechanol. 1, 7–11 (2012)

    Google Scholar 

  18. Nvidia, “Jetson nano develop kit”, [Online]. Available: https:// developer.nvidia.com/embedded/jetson-nano-developer-kit.

  19. STMicroelectronics, “STM32F427xx STM32F429xx”, STM32F429ZI datasheet, Jan. 2018 [Revised May 2021].

Download references

Acknowledgements

The authors also gratefully acknowledge the helpful comments and suggestions of the Editor and anonymous reviewers, which had improved the presentation of this paper.

Funding

Research supported by the National Science and Technology Council of Taiwan, under Grant MOST 111–2221-E-110–061-.

Author information

Authors and Affiliations

  1. Department Electrical Engineering, National University of Kaohsiung, Kaohsiung, 811, Taiwan

    Hung-Yi Lin

  2. Department of Electrical Engineering, National Sun Yat-sen University, Kaohsiung, 80424, Taiwan

    Shun-Hung Tsai

  3. Graduate Institute of Automation Technology, National Taipei University of Technology, Taipei, 10608, Taiwan

    Shun-Hung Tsai & Kuan-Yo Chen

Authors
  1. Hung-Yi Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shun-Hung Tsai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kuan-Yo Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hung-Yi Lin.

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

Lin, HY., Tsai, SH. & Chen, KY. Design and Implementation of the Trajectory Tracking and Dynamic Obstacle Avoidance of Wheeled Mobile Robot Based on T–S Fuzzy Model. Int. J. Fuzzy Syst. 25, 2423–2438 (2023). https://doi.org/10.1007/s40815-023-01523-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40815-023-01523-z

Keywords

Search

Navigation